NLTK & Natural Language Generation 2020


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100 Best NLTK VideosJoBimTextNLTK & Chatbots | NLTK & Dialog Systems


Logical natural language generation from open-domain tables
W Chen, J Chen, Y Su, Z Chen, WY Wang – arXiv preprint arXiv …, 2020 – arxiv.org
… Verify: Refuted Figure 3: Evaluation of surface-level generation vs. logical natural language generation … The fluency evaluation is simply based on the standard metrics like Perplexity (Ben- gio et al., 2003) and BLEU-1,2,3 (Papineni et al., 2002) based on NLTK (Bird, 2006) …

Creation of Intellectual Property Based on Natural Language Generation Model
AON Rene, T Matsui, S Onoda… – 2020 Joint 11th …, 2020 – ieeexplore.ieee.org
… We used spacy, a natural language processing toolkit in Python and NLTK, a tool for speech tagging, plus termextract a software developed jointly by the University of Tokyo and Yokohama National University for extraction of technical terms and comparison models, etc …

Using Natural Language Generation to Bootstrap Missing Wikipedia Articles: A Human-centric Perspective
LA Kaffee, P Vougiouklis… – Semantic Web …, 2020 – semantic-web-journal.net
… 47 48 48 49 49 50 50 51 51 Using Natural Language Generation to Bootstrap Missing Wikipedia Articles: A Human-centric Perspective … Abstract. Nowadays natural language generation (NLG) is used in everything from news reporting and chatbots to social media management …

Schema-Guided Natural Language Generation
Y Du, S Oraby, V Perera, M Shen… – arXiv preprint arXiv …, 2020 – arxiv.org
… nrynchn2}@illinois.edu Abstract Neural network based approaches to natural language generation (NLG) have gained pop- ularity in recent years. The goal of … lower the bet- 7We use NLTK for BLEU4/METEOR (Bird et al., 2009). ter).8 It …

Stochastic Natural Language Generation Using Dependency Information
E Seifossadat, H Sameti – arXiv preprint arXiv:2001.03897, 2020 – arxiv.org
Page 1. Stochastic Natural Language Generation Using Dependency Information … 1. Introduction Natural language generation (NLG) is the task of generating a natural-language text from structured, formal and abstract meaning representation (MR) (Reiter and Dale 2000) …

Generation and evaluation of artificial mental health records for Natural Language Processing
J Ive, N Viani, J Kam, L Yin, S Verma, S Puntis… – NPJ Digital …, 2020 – nature.com
A serious obstacle to the development of Natural Language Processing (NLP) methods in the clinical domain is the accessibility of textual data. The mental health domain is particularly challenging, partly because clinical documentation relies heavily on free text that is difficult to …

Russian Natural Language Generation: Creation of a Language Modelling Dataset and Evaluation with Modern Neural Architectures
Z Shaheen, G Wohlgenannt, B Zaity… – arXiv preprint arXiv …, 2020 – arxiv.org
… human-like text is challenging, it includes modeling high-level syntactic properties and features like sentiment and topic [1]. Natural Language Generation (NLG) produces … 2https://github.com/ yutkin/Lenta.Ru-News-Dataset 3https://nltk.org 4https://github.com/Mottl/ru_punkt 5 …

A survey on chatbot implementation in health care using NLTK
JJ Sophia, DA Kumar, M Arutselvan… – Int. J. Comput. Sci. Mob …, 2020 – academia.edu
… We can use Natural Language Processing by importing one of the python modules – Natural Language Toolkit (NLTK) … In order to achieve an accurate diagnosis, the logic for state transitions are made, natural language generation templates were used, and system initiative to …

A general benchmarking framework for text generation
D Moussallem, P Kaur, TC Ferreira… – … Language Generation …, 2020 – aclweb.org
… 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), Dublin, Ireland (Virtual), 18 December 2020, pages 27–33, c … comparison, BENG uses two imple- mentations of BLEU: (1) Multi-bleu-detok from Moses,4 (2) BLEU-NLTK from the …

Deep Generative Models for Natural Language Generation
D Li – 2020 – search.proquest.com
… Page 2. Page 3. Deep Generative Models for Natural Language Generation Dianqi Li A dissertation submitted in partial fulfillment of the requirements for the degree of … Page 4. University of Washington Abstract Deep Generative Models for Natural Language Generation Dianqi Li …

Transformers: State-of-the-art natural language processing
T Wolf, J Chaumond, L Debut, V Sanh… – Proceedings of the …, 2020 – aclweb.org
… 2019. BART: Denoising Sequence-to-Sequence pre- training for natural language generation, translation, and comprehension … ArXiv, abs/1907.11692. Edward Loper and Steven Bird. 2002. NLTK: The nat- ural language toolkit …

Text-to-text pre-training model with plan selection for rdf-to-text generation
N Kertkeidkachorn, H Takamura – … on Natural Language Generation from …, 2020 – aclweb.org
… 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), Dublin, Ireland (Virtual), 18 December 2020, pages 159 … Metrics Our Approach Baseline Select No Select BLEU 50.93 50.43 40.57 BLEU NLTK 0.482 0.476 0.396 METEOR 0.384 …

Design and Implementation of an Automatic Summarizer Using Extractive and Abstractive Methods
A Chattopadhyay, M Dey – Proceedings of the Global AI Congress 2019, 2020 – Springer
… There are three categories to computational linguistics: natural language processing (NLP), natural language understanding (NLU) and natural language generation (NLG) … For our NLP tasks, we have tried out using both NLTK and spaCy libraries in Python, separately, for our …

Using Natural Language Generation to Bootstrap Empty Wikipedia Articles: A Human-centric Perspective
LA Kaffee, P Vougiouklis, E Simperl – semantic-web-journal.net
… 47 48 48 49 49 50 50 51 51 Using Natural Language Generation to Bootstrap Empty Wikipedia Articles: A Human-centric Perspective … Abstract. Nowadays natural language generation (NLG) is used in everything from news reporting and chatbots to social media management …

STYLIZED NATURAL LANGUAGE GENERATION IN DIALOGUE SYSTEMS
K BOLSHAKOVA – dspace.vutbr.cz
… DEPARTMENT OF COMPUTER GRAPHICS AND MULTIMEDIA ÚSTAV PO?ÍTA?OVÉ GRAFIKY A MULTIMÉDIÍ STYLIZED NATURAL LANGUAGE GENERATION IN DIALOGUE SYSTEMS … Page 5. Stylized Natural Language Generation in Dialogue Systems Declaration …

PoetryMirror: An Affective, Poetic Reflection of You Through Natural Language Generation
S Aslam, V Batenburg – researchgate.net
… 2 Related work Many other natural language generation projects focused on poetry exist … An implementation can be found in NLTK (Steven et al., 2009). VADER, though not perfect, VADER is still state of the art in natural language sentiment analysis …

A Novel Approach to Interactive Dialogue Generation Based on Natural Language Creation with Context-Free Grammars and Sentiment Analysis
F Palmas, J Raith – 2020 IEEE 20th International Conference …, 2020 – ieeexplore.ieee.org
… The use of natural language generation creates a high number of variations for otherwise similar or repetitive sentences … B. Natural Language Toolkit The Natural Language Toolkit (NLTK) is a collection of open-source Python libraries and interfaces for a multitude of NLP tasks …

A Hybrid Text Classification and Language Generation Model for Automated Summarization of Dutch Breast Cancer Radiology Reports
E Nguyen, D Theodorakopoulos… – 2020 IEEE Second …, 2020 – ieeexplore.ieee.org
… One of the first works using seq2seq models for natural language generation was done by Sutskever, Vinyals and Le [13] … As we are dealing with a Dutch corpus, the Dutch stop words from NLTK [19] were used for this task. A tokenizer is used to create the vocabulary …

ProBot–A Procedure Chatbot for Digital Procedural Adherence
N Ade, N Quddus, T Parker… – Proceedings of the …, 2020 – journals.sagepub.com
… set of queries from the operator and text from the digital procedures through deep learning and provides responses using natural language generation … in the dataset and mainly involves cleaning the dataset using natural language processing through the nltk Python module …

Turn-Level Recurrence Self-attention for Joint Dialogue Action Prediction and Response Generation
Y Tan, Z Ou, K Liu, Y Shi, M Song – Asia-Pacific Web (APWeb) and Web …, 2020 – Springer
… 12], semantically controlled Natural Language Generation is a more comprehensive component which contains Dialogue Action Prediction and Natural Language Generation, assuming that … use is the same as the dataset benchmark 1 and BLEU metrics we use is from NLTK 2 …

An Interpretation of Lemmatization and Stemming in Natural Language Processing
D Khyani, BS Siddhartha, NM Niveditha, BM Divya – jusst.org
… Division of NLP – Natural Language Generation and Natural Language Understanding Natural Language Generation: Producing meaningful phrases and sentences in the … NLTK has a PorterStemmer class with the help of which we can implement the Porter Stemmer Algorithm …

Towards learning visual semantics
H Cai, SR Pant, W Li – Proceedings of the 28th ACM Joint Meeting on …, 2020 – dl.acm.org
… 2.4 Natural Language Generation (NLG) The NLG module aims to turn the visual content extracted from the visual outputs into a natural-language-like … The individual sentences generated were then summarized to construct concise semantics descriptions using NLTK [17]. 1539 …

Mitigating File-Injection Attacks with Natural Language Processing
H Liu, B Wang – Proceedings of the Sixth International Workshop on …, 2020 – dl.acm.org
… Science Dataset. This dataset is one of the datasets o ered in Natural Language Toolkit (NLTK) [26]. It consists of science news from multiple sources, such as ABC science news. This dataset contains 16,043 sentences, 366,787 words and 1,862,913 characters …

The 2020 bilingual, bi-directional webnlg+ shared task overview and evaluation results (webnlg+ 2020)
T Ferreira, C Gardent, N Ilinykh… – … Natural Language …, 2020 – hal.archives-ouvertes.fr
… hand, we seek to provide a common benchmark on which to evaluate and compare “micro-planners”, ie, Natural Language Generation (NLG) systems … 2002), regular and with the Smoothing Function 3 proposed in (Chen and Cherry, 2014) (eg, BLEU NLTK); METEOR (Lavie …

Test Case Generation from Specifications Using Natural Language Processing
A Salman – 2020 – diva-portal.org
… analysis of languages for the purpose of producing a meaningful repre- sentation [31]. Page 19. CHAPTER 2. BACKGROUND 9 • Natural Language Generation (NLG): this sub-field refers to the pro- cess of producing natural language outputs from non-linguistic inputs [32] …

Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints
Z Wang, X Wang, B An, D Yu, C Chen – arXiv preprint arXiv:2005.00969, 2020 – arxiv.org
… In our case, key words are defined as nouns, which can be easily extracted with exist- ing tools such as NLTK (Loper and Bird, 2002) … We use NLTK (Loper and Bird, 2002) to extract the nouns that are then used for computing the OT loss …

Text mining ancient texts for human experience and wisdom in dealing with difficult periods of human history of plague and pestilence through a NLP based …
B Geoffrey, A Sanker, S Verma – 2020 – osf.io
… Fig.4 – Summary obtained by Abstractive NLP summarizer Fig.5 – Summary obtained by NLTK summarizer Page 8 … summarization of evaluative text: The effect of corpus controversiality.” Proceedings of the Fifth International Natural Language Generation Conference. 2008. 24 …

Analysis of Intelligent Machines using Deep learning and Natural Language Processing
MR Kounte, PK Tripathy, P Pramod… – 2020 4th International …, 2020 – ieeexplore.ieee.org
… B. Natural Language Generation NLG (Natural Language Generation) translates a machine’s artificial language into the text format, and also have the ability … analysis.[8] 1) Algorithm: For an efficient sentiment analysis, the Nat- ural Language Processing Toolkit (NLTK) is used …

Natural language processing (NLP) in management research: A literature review
Y Kang, Z Cai, CW Tan… – Journal of …, 2020 – orsociety.tandfonline.com
… into commands that can be executed by computers. NLP consists of two research directions: Natural Language Understanding (NLU) and Natural Language Generation (NLG). The principal mission of NLU is to comprehend …

Related Blogs’ Summarization With Natural Language Processing
N Baliyan, A Sharma – The Computer Journal, 2020 – academic.oup.com
… ratios is important. This can be achieved using simple regular expressions searches or by using Natural Language Toolkit (NLTK) library of Python to check and match into the collection of stop words. Stemming. Different words …

Autoregressive Knowledge Distillation through Imitation Learning
A Lin, J Wohlwend, H Chen, T Lei – arXiv preprint arXiv:2009.07253, 2020 – arxiv.org
… edu 4tao@asapp.com Abstract The performance of autoregressive models on natural language generation tasks has dramat- ically improved due to the adoption of deep, self-attentive architectures. However, these gains have …

Deep learning in clinical natural language processing: a methodical review
S Wu, K Roberts, S Datta, J Du, Z Ji, Y Si… – Journal of the …, 2020 – academic.oup.com
AbstractObjective. This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providin.

Acquaintance with Natural Language Processing for Building Smart Society
RP Bachate, A Sharma – E3S Web of Conferences, 2020 – e3s-conferences.org
… ASR). Natural Language Processing can be understood by classifying it into Natural Language Generation and Natural Language Understanding … world. Python includes NLP tools such as NLTK, Spacy, TextBlob, PyTorch, Texacy …

PREDICTING THE BEHAVIORAL CHARACTERISTIC OF EARLY REVIEWERS FOR EFFECTIVE PRODUCT MARKETING IN E-COMMERCE
V Gangabhavani, G Divya, C Sravani… – Machine … – jespublication.com
… Question Answering [37][38](IBM Watson’s answers to a query). • Natural Language Generation[39][40] (Generation of text from image or video data.) (Natural Language Toolkit)NLTK: NLTK is a popular open-source package in Python …

De-biased Court’s View Generation with Causality
Y Wu, K Kuang, Y Zhang, X Liu, C Sun, J Xiao… – Proceedings of the …, 2020 – aclweb.org
… In this paper, we propose a novel Attentional and Counterfac- tual based Natural Language Generation (AC- NLG) method, consisting of an attentional en- coder and a pair of innovative counterfactual decoders … 2.2 Natural Language Generation …

Leveraging pre-trained checkpoints for sequence generation tasks
S Rothe, S Narayan, A Severyn – Transactions of the Association for …, 2020 – MIT Press
Create a new account. Email. Returning user. Can’t sign in? Forgot your password? Enter your email address below and we will send you the reset instructions. Email. Cancel. If the address matches an existing account you will …

Natural language processing-enhanced extraction of SBVR business vocabularies and business rules from UML use case diagrams
P Danenas, T Skersys, R Butleris – Data & Knowledge Engineering, 2020 – Elsevier
JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …

Natural Language Information Extraction Through Non-Factoid Question and Answering System (NLIEQA Non-Factoid)
PS Banerjee, A Ghosh, A Gupta… – … Conference on Advanced …, 2020 – Springer
… NLTK also demands the presence of a noun just after any “ing” verb to identify that verb as a gerund in some cases while working with … In future research, the concept of natural language generation would be adopted for achieving better results like producing narrative answers …

Multi-lingual mathematical word problem generation using long short term memory networks with enhanced input features
V Liyanage, S Ranathunga – … of The 12th Language Resources and …, 2020 – aclweb.org
… tional Conference on Natural Language Generation, pages 188–197. Liyanage, V. and Ranathunga, S. (2019). A multi-language platform for generating algebraic mathematical word prob- lems. arXiv preprint arXiv:1912.01110. Loper, E. and Bird, S. (2002). Nltk: the natural …

RDFJSREALB: a Symbolic Approach for Generating Text from RDF Triples
G Lapalme – 2020 – rali.iro.umontreal.ca
… Dong and Holder (2014) present Natural Language Generation from Graphs (NLGG) with three processing stages: model preparation and content determination, document structur- ing, and lexicalization … tmp BLEU B.NLTK METEOR chrF++ 1-5 5378 7 % 0.41 0.39 0.40 0.64 …

Amplifying the Range of News Stories with Creativity: Methods and their Evaluation, in Portuguese
R Mendes, HG Oliveira – … Conference on Natural Language Generation, 2020 – aclweb.org
Proceedings of The 13th International Conference on Natural Language Generation, pages 252–262, Dublin, Ireland, 15-18 December, 2020 … but BERT, headlines and proverbs were tokenized with the NLPyPort package (Ferreira et al., 2019), a layer on top of NLTK (Loper and …

TLDR: token loss dynamic reweighting for reducing repetitive utterance generation
S Jiang, T Wolf, C Monz, M de Rijke – arXiv preprint arXiv:2003.11963, 2020 – arxiv.org
… It has been widely reported that Natural Language Generation (NLG) models are prone to generat- ing repetitive utterances, which is a cross-task and … For all three datasets, we tokenize the utterances using the NLTK (Loper and Bird, 2002) tokenizer and keep the most frequent …

Open domain event text generation
Z Fu, L Bing, W Lam – Proceedings of the AAAI Conference on Artificial …, 2020 – ojs.aaai.org
… Introduction In recent years, many natural language generation (NLG) tasks have been proposed to generate human-readable text based on different kinds of data. (Gardent et al … Candidate Extraction We split the article text into sentences with NLTK (Bird and Loper 2004) …

Evaluation of text generation: A survey
A Celikyilmaz, E Clark, J Gao – arXiv preprint arXiv:2006.14799, 2020 – arxiv.org
… Jianfeng Gao? Microsoft Research jfgao@microsoft.com Abstract The paper surveys evaluation methods of natural language generation (NLG) sys- tems that have been developed in the last few years … Contents 1 Introduction 4 1.1 Evolution of Natural Language Generation …

ARTIFICIAL INTELLIGENCE IN ONLINE SHOPPING USING NATURAL LANGUAGE PROCESSING (NLP)
D Selvapandian, RUN Mary… – Journal of Critical Reviews, 2020 – jcreview.com
… 3) Solution The web scraping methods is used to scrape the reviews and process the NLTK and convert it as tensor and pass it on to the model … 3. Shemtov, H. (1997). Ambiguity management in natural language generation. Stanford University …

Knowledge-aware attentive wasserstein adversarial dialogue response generation
Y Zhang, Q Fang, S Qian, C Xu – ACM Transactions on Intelligent …, 2020 – dl.acm.org
… Natural language generation has become a fundamental task in dialogue systems. RNN-based natural re- sponse generation methods encode the dialogue context and decode it into a response … Nowadays, these methods are adopted to natural language generation …

Deep Generation Techniques in Task-Oriented Dialogue Systems
L Shu – 2020 – indigo.uic.edu
… history for producing KB queries. Dialogue policy model decides on the system action which is then realized by a natural language generation component. The natural language generation component, particularly style-variation text generation …

Mining Implicit and Explicit Rules for Customer Data Using Natural Language Processing and Apriori Algorithm
T Velmurugan, B Hemalatha – 2020 – researchgate.net
… Techniques used in NLP understanding are Syntax and Semantics.Natural Language Generation includes utilizing repositories to extract and translate linguistic desires … In this proposed work Natural Language Tool Kit (NLTK) is used for preprocessing of review data and it is …

Applying Natural Language Processing techniques to analyze HIV-related discussions on Social Media
P Garza, R Sarvas, PDA Malik, A Angi – 2020 – webthesis.biblio.polito.it
Page 1. POLITECNICO DI TORINO DEPARTMENT OF CONTROL AND COMPUTER ENGINEERING Master of Science in Computer Engineering Master Degree Thesis Applying Natural Language Processing techniques to analyze HIV-related discussions on Social Media …

Policy-driven neural response generation for knowledge-grounded dialogue systems
B Hedayatnia, S Kim, Y Liu, K Gopalakrishnan… – arXiv preprint arXiv …, 2020 – arxiv.org
… This form of control is similar to dialogue management (DM) and natural language generation (NLG) in task-oriented systems where a meaning representation determined by the DM is realized as a … For sentence-tokenization we use the NLTK library (Loper and Bird, 2002) …

From semantics to pragmatics: where IS can lead in Natural Language Processing (NLP) research
Y Li, M Thomas, D Liu – European Journal of Information Systems, 2020 – Taylor & Francis
… between humans and computers generally comprise two branches of activities: natural language understanding (NLU) and natural language generation (NLG) … For example, NLTK, a leading platform for building Python-based NLP programs, implements a comprehensive set of …

Identification of Disaster-Related Tweets Using Natural Language Processing: International Conference on Recent Trends in Artificial Intelligence, IOT, Smart Cities & …
S Goswami, D Raychaudhuri – IOT, Smart Cities & Applications …, 2020 – papers.ssrn.com
… The authors Hamid Bagheri and Md Johirul Islam had developed a predictive model for sentiment analysis of twitter (Bagheri H , Islam Md J, 2017) [1]. They used Textblob python library for text processing and NLTK for Natural language Processing …

Interactive Transport Enquiry with AI Chatbot
M Dharani, J Jyostna, E Sucharitha… – … and Control Systems …, 2020 – ieeexplore.ieee.org
… b) Natural Language Generation: NLG is used for text planning, text mining, sentence planning, and text realization. Python consists of a library named NLTK (Natural Language ToolKit) which implements NLP. The following are the techniques present in NLP …

Out of Order: How important is the sequential order of words in a sentence in Natural Language Understanding tasks?
TM Pham, T Bui, L Mai, A Nguyen – arXiv preprint arXiv:2012.15180, 2020 – arxiv.org
… 12,683 (QQP). 3We used NLTK sentence splitter (Bird et al., 2009) to detect text that has more than one sentence. 3 Page 4. See Table A1 for the total number of examples remaining after each of the filtering steps above. 2.3 …

KGLM: Pretrained Knowledge-Grounded Language Model for Data-to-Text Generation
W Chen, Y Su, X Yan, WY Wang – … of the 2020 Conference on Empirical …, 2020 – aclweb.org
… 3.1 Hyperlinked Sentence Crawling We use English Wikidump2 as our data source. For each Wikipedia page, we split the whole paragraphs into an array of sentences and then tokenize with the nltk toolkit (Loper and Bird, 2002) …

Controllable Text Generation with Focused Variation
L Shu, A Papangelis, YC Wang, G Tur, H Xu… – arXiv preprint arXiv …, 2020 – arxiv.org
… As many researchers noted, injecting style into natural language generation can increase the naturalness and human-likeness of text by in- cluding pragmatic markers, characteristic of oral language (Biber, 1991; Paiva and Evans, 2004; Mairesse and Walker, 2007) …

KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation
W Chen, Y Su, X Yan, WY Wang – arXiv preprint arXiv:2010.02307, 2020 – arxiv.org
… Page 3. 3.1 Hyperlinked Sentence Crawling We use English Wikidump2 as our data source. For each Wikipedia page, we split the whole paragraphs into an array of sentences and then tokenize with the nltk toolkit (Loper and Bird, 2002) …

Anlizing the adversarial natural language inference dataset
A Williams, T Thrush, D Kiela – arXiv preprint arXiv:2010.12729, 2020 – arxiv.org
Page 1. ANLIzing the Adversarial Natural Language Inference Dataset Adina Williams, Tristan Thrush, Douwe Kiela Facebook AI Research {adinawilliams, tthrush, dkiela}@fb.com Abstract We perform an in-depth error analysis …

Bank Customer Complaints Analysis Using Natural Language Processing and Data Mining
GN Nikitha, C Chandana… – … in Science and …, 2020 – journals.grdpublications.com
… 4. Convert Sentence segmentation into Tokenization 5. The selected datasets are catharized using nltk library … Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation. D. Analysis …

REVIEW ON THE ROLE OF NATURAL LANGUAGE PROCESSING IN MAKING INTERACTIVE SYSTEMS
S Jacob, R Mathur, S Ghosh – irjmets.com
… a) NLTK: This is a massively feature rich toolbox which can be used to perform NLP related functions and activities, used for creating Python projects to work … Coherence, along with property of good text, is used to evaluate the output quality of natural language generation system …

Knowledge-grounded response generation with deep attentional latent-variable model
HT Ye, KL Lo, SY Su, YN Chen – Computer Speech & Language, 2020 – Elsevier
… We use the implementation in the Python package nlg-eval 3 for BLEU and METEOR scores (Sharma, El Asri, Schulz, Zumer, 2017), and the NLTK toolkit to calculate NIST scores. Our results are shown in Table 3 and Table 4 …

Capturing greater context for question generation
LA Tuan, D Shah, R Barzilay – Proceedings of the AAAI Conference on …, 2020 – ojs.aaai.org
… vec- tors (Pennington, Socher, and Manning 2014). Text is low- ercased and tokenized with NLTK. We tune the step of biat- tention used in encoder from {1, 2, 3} on the development set. During decoding, we used beam search …

An unsupervised joint system for text generation from knowledge graphs and semantic parsing
M Schmitt, S Sharifzadeh, V Tresp… – Proceedings of the 2020 …, 2020 – aclweb.org
… Thus, there is a need for methods, such as automatic natural language generation (“graph?text”), that support them … After preprocessing a text with NLTK’s default POS tagger (Loper and Bird, 2004) and re- moving stop words, we apply two simple heuristics to extract facts: (1 …

Unsupervised and Supervised Learning of ComplexRelation Instances Extraction in Natural Language
Z Wang – 2020 – repository.tudelft.nl
Page 1. Unsupervised and Supervised Learning of Complex Relation Instances Extraction in Natural Language Version of November 13, 2020 Zina Wang Page 2. Page 3. Unsupervised and Supervised Learning of Complex Relation Instances Extraction in Natural Language …

AtheNA an avid traveller using LSTM based RNN architecture
A Acharya, Y Sneha, A Khettry, D Patil – J. Eng. Sci. Technol, 2020 – jestec.taylors.edu.my
… Programming Interface IDS Interest detection System IRCTC Indian Railway Catering and Tourism Corporation JSON JavaScript Object Notation LSTM Long short-term memory NLG Natural Language generation NLP Natural Language Processing NLTK Natural Language …

Natural Language Processing based Text Imputation for Malayalam Corpora
A Rojan, E Alias, GM Rajan, J Mathew… – … on Electronics and …, 2020 – ieeexplore.ieee.org
… The English pre-training is already an easier task due to the help of a variety of libraries such as TensorFlow, scipy, nltk etc … will have the option to gain from the data on the web and apply what they realized in reality Combined with natural language generation, computers will …

Generationary or:“How We Went beyond Word Sense Inventories and Learned to Gloss”
M Bevilacqua, M Maru, R Navigli – … of the 2020 Conference on Empirical …, 2020 – aclweb.org
… dictionary “definition”. Page 2. 7208 WiC). This, in turn, results in a more solid assess- ment of the generation quality, a notorious problem in Natural Language Generation (NLG) evaluation (Gatt and Krahmer, 2018). In contrast …

Manifesting construction activity scenes via image captioning
H Liu, G Wang, T Huang, P He, M Skitmore… – Automation in …, 2020 – Elsevier
… Image captioning, which is rooted in computer vision (CV) and natural language generation (NLG), provides a solution by automatically representing scene information with both … The part-of-speech distributions of Dataset I and Dataset II are compared by using the NLTK toolkit [.

NUIG-DSI at the WebNLG+ challenge: Leveraging Transfer Learning for RDF-to-text generation
N Pasricha, M Arcan… – … Language Generation …, 2020 – webnlg-challenge.loria.fr
… 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), Dublin, Ireland (Virtual), 18 December 2020, pages 137 … all seen unseen entities categories BLEU 51.74 58.26 52.76 45.57 BLEU NLTK 0.514 0.579 0.523 0.454 METEOR 0.403 …

The CACAPO Dataset: A Multilingual, Multi-Domain Dataset for Neural Pipeline and End-to-End Data-to-Text Generation
C van der Lee, C Emmery, S Wubben… – … Language Generation, 2020 – aclweb.org
Page 1. Proceedings of The 13th International Conference on Natural Language Generation, pages 68–79, Dublin, Ireland, 15-18 December, 2020. c 2020 Association for Computational Linguistics 68 The CACAPO Dataset …

Natural Language Processing for Disaster Management Using Conditional Random Fields
H Ketmaneechairat, M Maliyaem – Journal of Advances in Information …, 2020 – jait.us
… The challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation … It’s similar to Natural Language Toolkit (NLTK) but PythaiNLP is focus on Thai language …

Analyzing Fake News Based on Machine Learning Algorithms
PA Ba, JM Aa, KD Na – Intelligent Systems and Computer …, 2020 – books.google.com
… Analyzing Fake News Based on Machine Learning Algorithms consist of natural language understanding, natural language processing and natural language generation … with the help of python programming using Spacy package which is found better than NLTK NLP package …

Teaching Natural Language Processing through Big Data Text Summarization with Problem-Based Learning
L Li, J Geissinger, WA Ingram… – Data and Information …, 2020 – content.sciendo.com
Jump to Content Jump to Main Navigation …

Automatic Coherent and Concise Text Summarization using Natural Language Processing
K Muthiah – 2020 – norma.ncirl.ie
… This is due to the fact that abstractive summarization techniques deals with issues such as semantic representation and natural language generation which are commensurately … Then, we use sent tokenize module from the nltk.tokenize package of the NLTK8 python library …

Role of Computational Intelligence in Natural Language Processing
BR DAS, BK MISHRA – Natural Language Processing in Artificial …, 2020 – books.google.com
… natural language data. Two major things are there, one is natural language understanding (NLU) and natural language generation (NLG). NLP is purely based on grammar- based (rule-based) and statistical-based. In the early …

Universal Sentence-Embedding Models
MY Day, C Jou – Foundations, 2020 – mail.tku.edu.tw
… Part-of-Speech (POS) Dependency Parser Source: Nitin Hardeniya (2015), NLTK Essentials, Packt Publishing; Florian Leitner (2015), Text mining – from Bayes rule to dependency parsing Sentence Segmentation … Page 27. Turing Natural Language Generation (T-NLG) 27 …

Natural language processing
RST Lee – Artificial Intelligence in Daily Life, 2020 – Springer
… Natural Language Generation (NLG) … various functions and algorithms on syntactic and semantic analysis have been implemented in various NLP-related development platforms as functional libraries or packages that include C, C++ , Perl, ADW in Java, and NLTK in Python …

Conditional rap lyrics generation with denoising autoencoders
NI Nikolov, E Malmi, CG Northcutt, L Parisi – arXiv preprint arXiv …, 2020 – arxiv.org
… This type of noise forces our models to learn to rearrange the location of the input content words when gen- erating the output rap lyric, rather than to merely copy words from the input in an identical order. 1We use the list of English stopwords defined in NLTK …

Hybrid Machine learning for computational linguistics
P Dell’Aversana – researchgate.net
… In other words, semantic 2 Corpora are large bodies of linguistic data, 3 NLTK is downloadable from http://www.nltk.org/. Page 5. 5 … Natural language generation: it uses databases to derive semantic intentions and converts them into human language …

Contextual Chatbot for Healthcare Purposes (using Deep Learning)
P Kandpal, K Jasnani, R Raut… – 2020 Fourth World …, 2020 – ieeexplore.ieee.org
… For this work we have combined the concepts of TensorFlow, TFLearn, NLTK & NumPy with the field of healthcare … well as some difficult queries because they are powered with modern day technologies like Machine Learning, Natural Language Generation, Natural language …

A CONTEMPORARY SURVEY ON INTELLIGENT HUMAN-ROBOT INTERFACES FOCUSED ON NATURAL LANGUAGE PROCESSING
I Giachos, D Piromalis, M Papoutsidakis, S Kaminaris… – 2020 – researchgate.net
… WOZ is used to simulate ASR, MT (machine translation), NLU and natural language generation (NLG) as well as TTS … The basic natural language processing (NLP) pipeline of the system involves the NLTK parser (URL 24), an analyzer for the resulting parse similar to the Spatial …

Natural language understanding in argumentative dialogue systems
PR Shigehalli – 2020 – oparu.uni-ulm.de
Page 1. Natural Language Understanding in Argumentative Dialogue Systems Master Thesis by Pavan Rajashekhar Shigehalli Reviewer: Prof. Dr. Dr.-Ing. Wolfgang Minker Co-Reviewer: Prof. Dr.-Ing. Dr. hc Stefan Wesner Supervisor: M.Sc. Niklas Rach …

Semi-Automated Protocol Disambiguation and Code Generation
J Yen, T Lévai, Q Ye, X Ren, R Govindan… – arXiv preprint arXiv …, 2020 – arxiv.org
Page 1. Semi-Automated Protocol Disambiguation and Code Generation Jane Yen University of Southern California yeny@usc.edu Tamás Lévai Budapest University of Technology and Economics levait@tmit.bme.hu Qinyuan …

A Cue Adaptive Decoder for Controllable Neural Response Generation
W Wang, S Feng, W Gao, D Wang… – Proceedings of The Web …, 2020 – dl.acm.org
… CCS CONCEPTS • Computing methodologies ? Artificial intelligence; Natu- ral language processing; Natural language generation … All datasets are tokenized using the NLTK tokenizer [3], and all the initial weights are sampled from a uniform distribution [-0.08, 0.08] …

Learning to Ask More: Semi-Autoregressive Sequential Question Generation under Dual-Graph Interaction
Z Chai, X Wan – Proceedings of the 58th Annual Meeting of the …, 2020 – aclweb.org
Page 1. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 225–237 July 5 – 10, 2020. c 2020 Association for Computational Linguistics 225 Learning to Ask More: Semi-Autoregressive …

ProphetNet-Ads: A Looking Ahead Strategy for Generative Retrieval Models in Sponsored Search Engine
W Qi, Y Gong, Y Yan, J Jiao, B Shao, R Zhang… – … Conference on Natural …, 2020 – Springer
… However, simply adding a Trie constraint to a natural language generation (NLG) model is not enough, and we found several common problems in daily use … Okapi BM25 [12] is a traditional IR strategy, with the word tokenization of nltk [10] and parameters as \(k_1=1.2,b=0.75 …

Semantic Triples Verbalization with Generative Pre-Training Model
P Blinov – … Natural Language Generation from the Semantic …, 2020 – webnlg-challenge.loria.fr
… 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), Dublin, Ireland (Virtual), 18 December 2020, pages 154–158, c … Metric dev test synthetic BLEU 35.7 43.1 38.5 ± 0.8 BLEU NLTK 35.4 43.0 38.9 ± 0.8 METEOR 51.6 57.6 60.0 ± 0.5 …

Draft and edit: Automatic storytelling through multi-pass hierarchical conditional variational autoencoder
MH Yu, J Li, D Liu, D Zhao, R Yan, B Tang… – Proceedings of the AAAI …, 2020 – ojs.aaai.org
… For training, we pre- processed the ROCStories by applying NLTK for tokeniza- tion and split the processed data into 8:1:1 … Sequence to Sequence model (Sutskever, Vinyals, and Le 2014), that has been applied to a significant number of natural language generation tasks and …

Intelligent System for Semantically Similar Sentences Identification and Generation Based on Machine Learning Methods.
P Zdebskyi, V Lytvyn, Y Burov, Z Rybchak, P Kravets… – COLINS, 2020 – ceur-ws.org
… This includes speech recognition produced by the human voice. 4. Natural language generation: converting computer data into human natural lan- guage … The current system will not use any hardware interfaces. 6. Software interfaces: NLTK; PyTorch; Keras …

Towards human-chatbot interaction: a virtual assistant for the ramp-up process
M Zimmer, A Al-Yacoub, P Ferreira, N Lohse – repository.lboro.ac.uk
… Keywords—Ramp-up Process, Natural Language Processing, Natural Language Generation, Chatbot, Decision-support, Industry 4.0 … 3). Where the human uses the keyboard to type and send a message, the chatbot uses other Python libraries such as NLTK, Keras and …

FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation
K Lakhotia, B Paranjape, A Ghoshal, W Yih… – arXiv preprint arXiv …, 2020 – arxiv.org
… The above settings are used, both for intermediate fine-tuning as well as for end- task fine-tuning. For segmenting Wikipedia pas- sages into sentences for NQ, we use the pre-trained Punkt (Kiss and Strunk, 2006) sentence segmenter for English from the nltk library …

Discovering Useful Sentence Representations from Large Pretrained Language Models
N Subramani, N Suresh – arXiv preprint arXiv:2008.09049, 2020 – arxiv.org
… This is relevant because auto- regressive natural language generation has a very strong left-to-right tendency due to decoding oc- curring left-to … We choose NLTK’s Gutenberg dataset for our books portion, which consists of a subset of texts from Project Gutenberg (Lebert, 2008 …

Machine Learning Evaluation of Natural Language to Computational Thinking: On the possibilities of coding without syntax
D Björkman – 2020 – diva-portal.org
… analyse and handle [19]. These methods can instruct computers to analyse and handle natural language data. Examples where NLP is being heavily used are speech recognition and natural language generation. Be- low I will …

WebNLG 2020 Challenge: Semantic Template Mining for Generating References from RDF
T Tran, DT Nguyen – … on Natural Language Generation …, 2020 – webnlg-challenge.loria.fr
… 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), Dublin, Ireland (Virtual), 18 December 2020, pages 177–185, c … SYSTEM ID BLEU BLEU NLTK METEOR CHRF++ TER BERT PRECISION BERT RECALL BERT F1 BLEURT …

Extending Drag-and-Drop Actions-Based Model-to-Model Transformations with Natural Language Processing
P Danenas, T Skersys, R Butleris – Applied Sciences, 2020 – mdpi.com
Model-to-model (M2M) transformations are among the key components of model-driven development, enabling a certain level of automation in the process of developing models. The developed solution of using drag-and-drop actions-based M2M transformations contributes to …

Acrostic Poem Generation
R Agarwal, K Kann – arXiv preprint arXiv:2010.02239, 2020 – arxiv.org
… We use 80%, 10%, and 10% of the data for training, development, and test, respectively. We tokenize all poems with the NLTK WordTree- Bank tokenizer package (Loper and Bird, 2002). UnknownTopicPoems … All poems are tokenized with the help of NLTK. Sonnets …

Modeling Coherency in Generated Emails by Leveraging Deep Neural Learners
A Das, RM Verma – arXiv preprint arXiv:2007.07403, 2020 – arxiv.org
… The global coherency of the synthesized text is evaluated using a qualitative study as well as multiple quantitative measures. Keywords. Bidirectional LSTM, Doc2Vec embeddings, Proactive defense, natural language generation (NLG). 1 Introduction …

Overview of BioASQ 2020: The Eighth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
A Nentidis, A Krithara, K Bougiatiotis… – … Conference of the Cross …, 2020 – Springer
… NCU-IISR. B. BioBERT, logistic regression, LTR. UoT. B. BioBERT, multi-task learning, BC2GM. BioNLPer. B. BioBERT, multi-task learning, NLTK, ScispaCy. LabZhu. B. BERT, BoiBERT, XLNet, SpanBERT, transfer learning, SQuAD, ensembling. MQ. B …

LucidDream: Dynamic Story Generation through Directed Chatbot Interactions
R Stonebraker – 2020 – scholarworks.alaska.edu
… ysis, most notably the Python Natural Language Tool Kit (NLTK) library. However, it is not hard to find an ambiguous sentence that NLTK can’t eas 7 Page 10 … 2.2 Text G eneration Natural Language Generation (NLG) is often referred to as a the inverse of NLU …

Natural Language Processing for the Identification of Human Factors in Aviation Accidents Causes: An Application to the SHEL Methodology
G Perboli, SL Giudice, M Gajetti, S Fedorov – 2020 – cirrelt.ca
Page 1. CIRRELT-2020-36 Natural Language Processing for the Identification of Human Factors in Aviation Accidents Causes: An Application to the SHEL Methodology Guido Perboli Marco Gajetti Stanislav Fedorov Simona Lo Giudice September 2020 Page 2 …

Conversational AI-A Retrieval Based Chatbot
A Surendran, R Murali, RKR Babu – 2020 – login.easychair.org
… Here spell checking module is implemented by using NLTK spell checking module and a dictionary concept which contains a large … picking the responses from repository, the system can be made to produce answers by in-coorperating ideas of Natural Language Generation …

Computational generation of slogans
K Alnajjar, H Toivonen – Natural Language Engineering, 2020 – cambridge.org
… Our work contributes to the fields of Natural Language Processing (NLP) and Natural Language Generation (NLG) in two ways. On the one hand, this work computationally processes and generates short expressions … ”). Here we use stop words lists from NLTK …

Code to Comment “Translation”: Data, Metrics, Baselining & Evaluation
D Gros, H Sezhiyan, P Devanbu… – 2020 35th IEEE/ACM …, 2020 – ieeexplore.ieee.org
… Prior work in natural language generation has shown that infor- mation retrieval (IR) methods can be effective ways of producing suitable outputs … The authors’ imple- mentation is based off NLTK [36] using its “method 4” smoothing. This smoothing is more complex …

Attention history-based attention for abstractive text summarization
H Lee, YS Choi, JH Lee – Proceedings of the 35th Annual ACM …, 2020 – dl.acm.org
… CCS CONCEPTS • Computing methodologies ? Artificial intelligence; Natural language processing ? Natural language generation … We first identify the POS class of every word in the input articles and the ground truth summaries using the NLTK package (Loper and Bird, 2002 …

Corpus generation for voice command in smart home and the effect of speech synthesis on End-to-End SLU
T Desot, F Portet, M Vacher – 12th Conference on …, 2020 – hal.archives-ouvertes.fr
… The core of our corpus generator is the open source NLTK python library feature-based context free grammar (FCFG) (Bird et al., 2009), allowing for sentence generation, and for fea- tures (ie slot information) to be attached to the final output sentences …

Planning and Generating Natural and Diverse Disfluent Texts as Augmentation for Disfluency Detection
J Yang, D Yang, Z Ma – Proceedings of the 2020 Conference on …, 2020 – aclweb.org
Page 1. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 1450–1460, November 16–20, 2020. c 2020 Association for Computational Linguistics 1450 Planning and Generating …

UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation
J Guan, M Huang – arXiv preprint arXiv:2009.07602, 2020 – arxiv.org
… decoder paradigm (Sutskever et al., 2014), transformer-based architecture (Vaswani et al., 2017) and large-scale pretraining models (De- vlin et al., 2019; Radford et al., 2019) in a wide array of natural language generation (NLG) tasks … And we adopt NLTK4 for POS tagging …

Exploring Artificial Jabbering for Automatic Text Comprehension Question Generation
T Steuer, A Filighera, C Rensing – European Conference on Technology …, 2020 – Springer
… These findings started a debate in the natural language generation community if the model’s generation capabilities are to easy to misuse and therefore the models should not be released anymore [28] … Footnotes. 1. Using NLTK-3.4.5 …

Optimization of State of the Art Techniques for Natural Language Understanding
AA Fabiano – 2020 – oa.upm.es
… Information Extraction (IE). One of the most famous tools used to apply this process is the NLTK library [11], maintained by Princeton University and reference for several IE and IR techniques. 2.1.1.1 Text cleanup Prior to diving …

Design and development of diagnostic Chabot for supporting primary health care systems
B Kidwai, RK Nadesh – Procedia Computer Science, 2020 – Elsevier
… The techniques involve the use of Human-Computer Speech interaction, Natural Language Toolkit (NLTK) and the identification … The application uses high-level language processing (NLP) and natural language generation (NLG) methods to understand and generate dialogues …

Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders
NI Nikolov, E Malmi, C Northcutt, L Parisi – … Natural Language Generation, 2020 – aclweb.org
Page 1. Proceedings of The 13th International Conference on Natural Language Generation, pages 360–373, Dublin, Ireland, 15-18 December, 2020. c 2020 Association for Computational Linguistics 360 … 2We use the list of English stopwords defined in NLTK …

Comparing Different Methods for Assigning Portuguese Proverbs to News Headlines
R Mendes, HG Oliveira – Procs. of 11th ICCC, ICCC, 2020 – computationalcreativity.net
… For most of the methods, both headlines and proverbs were first pre-processed with the NLPyPort package (Fer- reira, Gonçalo Oliveira, and Rodrigues, 2019), a layer on top of the Natural Language Toolkit (NLTK) (Loper and Bird, 2002) tackling Portuguese, specifically …

E-commerce Query-based Generation based on User Review
Y Liu, KY Lee – arXiv preprint arXiv:2011.05546, 2020 – arxiv.org
… We propose a model utilizing attention and copy mechanism to perform jointly- conditional natural language generation based on … Our model is implemented using Pytorch2. Data tokenization is conducted using NLTK3. We set the hidden size of both the encoder and decoder …

Generátor filmových dialog? s využitím neuronových sítí.
Z Mukanova – 2020 – dspace5.zcu.cz
… Page 5. Abstract Natural language generation (NLG) is an area located at the intersection of artificial intelligence and computational linguistics … 56 Page 8. 1. Introduction Natural Language Generation (NLG) is a subsection of Natural Language Processing (NLP) [Yse20] …

Reinforcement Learning with Imbalanced Dataset for Data-to-Text Medical Report Generation
T Nishino, R Ozaki, Y Momoki, T Taniguchi… – Proceedings of the …, 2020 – aclweb.org
Page 1. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pages 2223–2236 November 16 – 20, 2020. c 2020 Association for Computational Linguistics 2223 Reinforcement …

Zero-Resource Knowledge-Grounded Dialogue Generation
L Li, C Xu, W Wu, Y Zhao, X Zhao, C Tao – arXiv preprint arXiv:2008.12918, 2020 – arxiv.org
… we build the knowledge corpus with a Wikipedia dump,2 where text is extracted with an open source tool3 and split into sentences using NLTK.4 In … Unsupervised learning and learning from zero resource have attracted widespread attention in natural language generation tasks …

Referring Expression Generation via Visual Dialogue
L Li, Y Zhao, Z Zhang, T Niu, F Feng… – … Conference on Natural …, 2020 – Springer
… same referent, and G be the words in REGer’s utterances. We first use the NLTK toolbox to exclude stopping words in \(re_{i}\) then build entity set \(e_i\) basing on the remaining words. If \(\exists i \in \left\{ 1,\ldots ,k \right\} , G …

Leveraging Large Pretrained Models for WebNLG 2020
X Li, A Maskharashvili, SJ Stevens-Guille… – … Language Generation …, 2020 – aclweb.org
… 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), Dublin, Ireland (Virtual), 18 December 2020, pages 117–124 … Task Test Subset BLEU BLEU NLTK METEOR CHRF++ TER BERT PRECISION BERT RECALL BERT F1 BLEURT …

Exploratory Data Analysis and Sentiment Analysis on Brazilian E-Commerce Website
M Patel – 2020 – soar.suny.edu
… help of simple sentiment analysis of natural language processing. Tasks of natural language processing range from simple sentiment analysis to natural language generation. There are quite a few … [26] NLTK: NLTK is a leading platform for building Python programs to work with …

Spatial Attention as an Interface for Image Captioning Models
P Sadler – arXiv preprint arXiv:2010.11701, 2020 – arxiv.org
… BLEU Bi-Lingual Evaluation Understudy CNN Convolutional Neural Network LSTM Long Short-Term Memory Network MSCOCO Microsoft Common Objects in Context NLTK Natural Language ToolKit ReLU Rectified Linear Unit RNN Recurrent Neural Network R-CNN Region …

Cue word guided question generation with BERT model fine-tuned on natural question dataset
Z Zhang – 2020 – researchcommons.waikato.ac.nz
… 2.4.6 Encoder The encoder for neural question generation model is based on document and answer embedding vectors. Document(context passage) and answer embedding vectors are sequences of tokens, created by NLTK tokenizer in preprocessing …

Using Recurrent Neural Networks for Part-of-Speech Tagging and Subject and Predicate Classification in a Sentence
D Muñoz-Valero, L Rodriguez-Benitez… – International Journal …, 2020 – atlantis-press.com
… The results obtained with each network improves the results obtained with other tools that we have used to compare with, tools like NLTK and pyStatParser [12] … With regard to natural language generation it can be considered a typical RNN application …

Are Some Words Worth More than Others?
S Dudy, S Bedrick – arXiv preprint arXiv:2010.06069, 2020 – arxiv.org
Page 1. Are Some Words Worth More than Others? Shiran Dudy Steven Bedrick Center for Spoken Language Understanding Oregon Health & Science University Portland, Oregon, USA {dudy,bedricks}@ohsu.edu Abstract Current …

Multilingual unsupervised sentence simplification
L Martin, A Fan, É de la Clergerie, A Bordes… – arXiv preprint arXiv …, 2020 – arxiv.org
… model. We extract sequences from raw documents using the NLTK sentence tok- enizer (Bird and Loper, 2004) and generate all pos- sible sequences of adjacent sentences with lengths between 10 and 300 characters. We …

“Talking” Triples to Museum Chatbots
S Varitimiadis, K Kotis, D Spiliotopoulos… – … Conference on Human …, 2020 – Springer
… Natural Language Processing (NLP) and Natural Language Generation (NLG) techniques enable computers to segment, assign meaning, and analyze human … The rest of the transformation is handled automatically with the use of the Natural Language ToolKit (NLTK), a python …

Exploring Artificial Jabbering For Automatic Text Comprehension Question Generation
C Rensing – kom.e-technik.tu-darmstadt.de
… These findings started a debate in the natural language generation community if the model’s generation capabilities are to easy to misuse and therefore the mod- els should not be released anymore [28] … 1 using NLTK-3.4.5 2 https://github.com/openai/gpt-2 Page 6 …

KPQA: A Metric for Generative Question Answering Using Word Weights
H Lee, S Yoon, F Dernoncourt, DS Kim, T Bui… – arXiv preprint arXiv …, 2020 – arxiv.org
Page 1. KPQA: A Metric for Generative Question Answering Using Word Weights Hwanhee Lee1, Seunghyun Yoon1, Franck Dernoncourt2 Doo Soon Kim2, Trung Bui2, Joongbo Shin1 and Kyomin Jung1 1Dept. of Electrical …

Semi-supervised Formality Style Transfer using Language Model Discriminator and Mutual Information Maximization
K Chawla, D Yang – arXiv preprint arXiv:2010.05090, 2020 – arxiv.org
… 4.3 Evaluation Metrics The result was evaluated with BLEU (Papineni et al., 2002). We used word tokenzier and corpus BLEU calculator from Natural Language Toolkit (NLTK) (Loper and Bird, 2002) to calculate the BLEU score …

Extracting graphstructured information from simple text
? ??????????? – 2020 – dspace.lib.uom.gr
… 20 6 The inheritance tree of Tokenizer1, source:Python 3 Text processing with NLTK 3 Cookbook … Machine Learning. The future of NLP is the Natural Language Generation (NLG). NLG is the technology that transforms data into natural language …

Closing the Loop Between Language and Vision for Embodied Agents
X Wang – 2020 – escholarship.org
Page 1. UC Santa Barbara UC Santa Barbara Electronic Theses and Dissertations Title Closing the Loop Between Language and Vision for Embodied Agents Permalink https://escholarship. org/uc/item/56087605 Author Wang, Xin Publication Date 2020 …

Neural generation of textual summaries from knowledge base triples
P Vougiouklis – 2020 – books.google.com
… Contents Chapter 1. Introduction 1 1.1 Aims and Objectives 2 1.2 Contributions 3 1.3 Thesis Structure 4 Chapter 2. Background 7 2.1 Natural Language Generation 7 2.2 Neural Networks in Natural Language Processing 10 2.2.1 Language Modelling with Neural Networks 11 …

Syntactically Guided Text Generation
Y Li – 2020 – smartech.gatech.edu
… margin. x Page 11. CHAPTER 1 INTRODUCTION Text generation, often referred to as natural language generation (NLG) is the task of gen- erating text with the target of simulating human-written text. Considered to be one of the …

Cooking recipes generator utilizing a deep learning-based language model
M Bie?, M Gilski, M Maciejewska, W Taisner – researchgate.net
… We then served the model in the form of a recipe generation website. A crucial part was evaluation of the generated text, that has been done by using NLG (Natural Language Generation) metrics as well as human based study. 1.2 Detailed project objective …

Referring Expression Generation via Visual Dialogue
F Feng, X Wang – … Processing and Chinese Computing: 9th CCF …, 2020 – books.google.com
… utterances. We first use the NLTK toolbox to exclude stopping words in re; then build entity set e; basing on the remaining words … eration. In: Proceedings of the 11th International Conference on Natural Language Generation, pp. 503–512 …

A domain-specific generative chatbot trained from little data
J Kapo?i?t?-Dzikien? – Applied Sciences, 2020 – mdpi.com
… Therefore, a focus of our research is on natural language understanding (NLU) (responsible for comprehension of user questions) and natural language generation (NLG) (responsible for producing answers in the natural language) modules …

Contextual text style transfer
Y Cheng, Z Gan, Y Zhang, O Elachqar, D Li… – arXiv preprint arXiv …, 2020 – arxiv.org
… Yang, 2004). After pre-processing and filtering with NLTK (Bird et al., 2009), we asked Amazon Mechanical Turk (AMT) annotators to identify in- formal sentences within each email, and rewrite them in a more formal style. Then …

Overview of BioASQ 8a and 8b: Results of the eighth edition of the BioASQ tasks a and b
A Nentidis, A Krithara, K Bougiatiotis, G Paliouras – 2020 – ceur-ws.org
… NCU-IISR B (exact, ideal) BioBERT, logistic regression, LTR UoT B (exact) BioBERT, multi-task learning, BC2GM BioNLPer B (exact) BioBERT, multi-task learning, NLTK, ScispaCy LabZhu B (exact) BERT, BioBERT, XLNet, SpanBERT, transfer learning, SQuAD, ensembling …

Review On Approaches for Theme Extraction and Sentence Ordering For Prioritization Of Journalistic Notes
D Wijesinghe, K Vidanage – 2020 International Conference on …, 2020 – ieeexplore.ieee.org
… [Online]. Available: http://ml-dl.com/nltk-vs-spacy/. [Accessed: 12-Nov-2019] … 82–88, doi: 10.1109/WI.2003.1241177. [27] K. Staykova, “Natural Language Generation and Semantic Technologies,” Cybern. Inf. Technol., vol. 14, no. 2, pp. 3–23, Jul …

Extractive text summarization of image extracted text
S Addya – 2020 – norma.ncirl.ie
… data there are several approach in data science, in that text analytics is focused on natural language processing and natural language generation … proposed research is using Python-tesseract to get extract the text from an image, then natural language toolkit (NLTK) is …

Automating Question Generation Given the Correct Answer
H Cao – 2020 – diva-portal.org
… tasks. Keywords Natural Language Processing, NLP, Natural Language Generation, NLG, Ques- tion Generation Page 4. iv Sammanfattning … Page 20. 12 CHAPTER 2. BACKGROUND 2.3 Natural language generation (NLG) Unlike …

Dataset Augmentation for Aspect Level Sentiment Analysis
N Sapru – 2020 – atrium.lib.uoguelph.ca
Page 1. Dataset Augmentation for Aspect Level Sentiment Analysis by Nikhil Sapru A thesis presented to the University of Guelph In partial fulfillment of requirements for the degree of Master of Applied Science in Engineering Guelph, Ontario, Canada c Nikhil Sapru, April, 2020 …

Variational Inference for Text Generation: Improving the Posterior
V Balasubramanian – 2020 – uwspace.uwaterloo.ca
… 2 2 Background 4 2.1 Natural Language Generation … 3 Page 17. Chapter 2 Background 2.1 Natural Language Generation Natural Language Generation in general is a broad area that primarily deals with gener- ation of natural language similar to humans …

Refer, Reuse, Reduce: Generating Subsequent References in Visual and Conversational Contexts
E Takmaz, M Giulianelli, S Pezzelle, A Sinclair… – arXiv preprint arXiv …, 2020 – arxiv.org
… We evaluate their output with metrics com- monly used in the domain of Natural Language Generation and with several linguistic measures … As for the generation models, we compute several metrics that are com- monly used in the domain of Natural Language Generation …

SLM: Learning a discourse language representation with sentence unshuffling
H Lee, DA Hudson, K Lee, CD Manning – arXiv preprint arXiv:2010.16249, 2020 – arxiv.org
… Our model is trained with 512 length-256 batch size using Wikipedia dumps and BookCorpus (Zhu et al., 2015). We split inputs into sentences using the NLTK toolkit (Loper and Bird, 2002), which are then re-shuffled for every epoch …

Intrinsic Evaluation of Summarization Datasets
R Bommasani, C Cardie – Proceedings of the 2020 Conference on …, 2020 – aclweb.org
… In the sibling subfield of machine translation, which often shares similar modelling challenges and evaluation regimes as summarization due to the shared nature of being sequence-to- sequence natural language generation tasks, the annual WMT conference3 consistently …

Text Summarization and Classification of Conversation Data between Service Chatbot and Customer
T Behere, A Vaidya, A Birhade, K Shinde… – 2020 Fourth World …, 2020 – ieeexplore.ieee.org
… It also removes abstracts with less than 3 keywords, then tokenize the abstract using NLTK Abstract graphs are then computed with nides … is that it gives better results since it focuses on the key sentences to be extracted rather than natural language generation by understanding …

Study of Extractive Text Summarizer Using The Elmo Embedding
H Gupta, M Patel – 2020 Fourth International Conference on I …, 2020 – ieeexplore.ieee.org
… Natural language processing has also its sub- branches 1) Natural language Understanding, 2)Natural language generation … There are many ways to splitting into sentences like using NLTK sentence tokenization, But our method gets more accuracy when the sentences are …

N-Gram Model
C Room – algorithms, 2020 – devopedia.org
… In general, many NLP applications benefit from N-gram models including part-of-speech tagging, natural language generation, word similarity, sentiment extraction and predictive text input … In Python, NTLK has the function nltk.utils.ngrams() …

NLP-assisted software testing: A systematic mapping of the literature
V Garousi, S Bauer, M Felderer – Information and Software Technology, 2020 – Elsevier
… of achieving human-like language processing” [8]. Challenges in NLP usually involve speech recognition, natural-language understanding, and natural-language generation … NLP tools are popular in this area, eg, the Stanford Parser [29], Natural Language Toolkit (NLTK) [30]. • …

Understanding Knowledge Gaps in Visual Question Answering: Implications for Gap Identification and Testing
G Bajaj, B Bandyopadhyay, D Schmidt… – Proceedings of the …, 2020 – openaccess.thecvf.com
… These metrics are commonly used for Natural Language Generation tasks … I: cap to the left of pants GQT: “What is the OBJ near the OBJ made of ?” 3https://www.nltk.org/_modules/nltk/translate/ bleu_score.html 4https://www.nltk.org/_modules/nltk/translate/ meteor_score.html …

Neural Question Generation with Transfer Learning and Utilization of External Knowledge
M Delpisheh – 2020 – yorkspace.library.yorku.ca
… UNK Unknown NLG Natural Language Generation CBOW Continuous Bag-Of-Words MLM Masked Language Model … Therefore, this problem have an ad- verse impact on many Natural Language Generation (NLG) tasks such as Neural Question Generation. 1.2 Contributions …

Part of Speech tagging for South African English
A Badenhorst – projects.cs.uct.ac.za
… language. NLP applications include speech recognition, understand- ing natural language, natural language generation, and ma- chine translation … languages. NLTK [23] is a natural language processing suite written in Python …

Evaluation Metrics for Text and Creation of Writing Tool for Sports Journalism
LMCL Correia – 2020 – repositorio-aberto.up.pt
… 2 2 Evaluation of NLG Systems 5 2.1 What is Natural Language Generation … 60 xiii Page 18. xiv LIST OF TABLES Page 19. Code Listings 5.1 Python example of tokenization, using word_tokenize method from the NLTK.tokenize library …

Chatbot development using Java tools and libraries
A Ili?, A Li?ina, D Savi? – 2020 24th International Conference on …, 2020 – ieeexplore.ieee.org
… Understanding which aims to make sense of language by enabling computers to read and comprehend and Natural Language Generation with the … B. Comparative analyzing the framework for chatbot development The Natural Language Toolkit (NLTK) is a suite of libraries and …

TECo: Automatic Selection and Adaptation of Creative Text in Context
RPP Mendes – 2020 – eg.uc.pt
… Keywords Computational Creativity, Artificial Intelligence, Linguistic Creativity, Natural Lan- guage Processing, Portuguese language, Word Embeddings, Semantic Similarity, Natural Language Generation vi … 1, 2, 4, 5, 8, 11, 12, 17, 20, 31, 60 NLTK Natural Language Toolkit. 32 …

An Approach for Journal Summarization Using Clustering Based Micro-Summary Generation
HA Mojeed, U Sanoh, SA Salihu, AO Balogun… – Proceedings of the …, 2020 – Springer
… However, the programs that can do this are harder to develop as they require the use of natural language generation technology, which itself is still a growing … The proposed approach was implemented in Python using the libraries NLTK, Scikit-Learn, NumPy, SciPy and Sympy …

Generation of sport news articles from match text commentary
D Porplenko – 2020 – core.ac.uk
Page 1 …

Impact of Positive Journalism: A Machine Learning Perspective
N Matta, S Pondichurri, S Jain, U Jain… – … Journal of Modern …, 2020 – modern-journals.com
… TextBlob 73% NLTK Vader 71% Fig 4: Sentiment Model Accuracy Comparison From Fig … “BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.” ArXiv abs/1910.13461 (2019): n.pag. 6. Lin, Chin-Yew. (2004) …

Generative adversarial networks for bias flipping
NK Le – 2020 – uis.brage.unit.no
… MLP Multi-Layer Perceptron NLP Natural Language Processing NLTK Natural Language Toolkit LSTM Long Short Term Memory … We also employ word_tokenize and sent_tokenize modules from Natural Language Toolkit (NLTK) [38] to do the tokenization. The 13 Page 30 …

Can You be More Social? Injecting Politeness and Positivity into Task-Oriented Conversational Agents
YC Wang, A Papangelis, R Wang, Z Feizollahi… – arXiv preprint arXiv …, 2020 – arxiv.org
… Concepts •Human-centered computing ? Natural language inter- faces; •Computing methodologies ? Discourse, dialogue and pragmatics; Natural language generation; … Messages were tokenized with the NLTK toolkit [6], and personally identifiable information (PII) such as …

Design and development of machine translation system for Maithili to English language
PK Singh – 2020 – shodhgangotri.inflibnet.ac.in
… It can be classified as 1. Natural Language Understanding : Computer understanding of human language is called Natural Language Understanding 2. Natural Language Generation: It is the developing … NLTK Toolkit, Stanford Core NLP , Unix code are used for tokenization …

Exploring the Potentiality of Semantic Features for Paraphrase Detection
RT Anchiêta, TAS Pardo – … on Computational Processing of the Portuguese …, 2020 – Springer
… For getting the WMD distance, we first tokenized and removed stopwords of the sentences, using the Natural Language Toolkit (NLTK) [6]; next, we got the … In: Proceedings of the 10th European Workshop on Natural Language Generation (ENLG-05) (2005)Google Scholar. 21 …

Detecting and Exorcising Statistical Demons from Language Models with Anti-Models of Negative Data
ML Wick, K Silverstein, JB Tristan, A Pocock… – arXiv preprint arXiv …, 2020 – arxiv.org
Page 1. Detecting and Exorcising Statistical Demons from Language Models with Anti-Models of Negative Data Michael Wick Oracle Labs michael.wick@oracle.com Kate Silverstein Oracle Labs kate.silverstein@oracle.com …

Application and techniques of opinion mining
N Gupta, R Agrawal – Hybrid Computational Intelligence …, 2020 – books.google.com
… Some of the most popular tools are: • Gate • NLTK • WEKA • Apache OpenNLP • Opinion Finder • Stanford CoreNLP • Pattern • LingPipe The following section covers … Noteworthy features of OpenNLP are: 1. Natural language generation 2. Summarize 3. NER 4. Tagging (POS) …

Language-Conditioned Feature Pyramids for Visual Selection Tasks
T Iki, A Aizawa – Proceedings of the 2020 Conference on Empirical …, 2020 – aclweb.org
… Model Detail We tokenized utterences by NLTK’s TweetTokenizer under case-insensitive conditions and omitted tokens appearing fewer than five times in the training dataset. We resized the photos to 224px square, regardless of their aspect ratio …

What Do You Mean ‘Why?’: Resolving Sluices in Conversations
VPB Hansen, A Søgaard – Proceedings of the AAAI Conference on …, 2020 – ojs.aaai.org
… Performance Metrics Natural language generation sys- tems are often evaluated in terms of BLEU scores (Papineni et al … 4We use the sentence-level GLEU and BLEU implementations provided by NLTK with the smoothing function introduced by Lin and Och (2004) …

Artificial intelligence in the battle against coronavirus (COVID-19): a survey and future research directions
TT Nguyen – arXiv preprint arXiv:2008.07343, 2020 – arxiv.org
… early stage of the pandemic. Alternatively, unstructured natural language data need text mining tools, eg Natural Language ToolKit (NLTK) [61], and advanced NLP and natural language generation (NLG) Page 6. 6 TABLE II …

Sentiment analysis of informal Malay tweets with deep learning
JY Ong, M Mun’im Ahmad Zabidi, N Ramli… – … International Journal of …, 2020 – core.ac.uk
… Natural Language Generation: The generation of natural language by a computer. – Speech Recognition: The translation of spoken language into text … “Automated Sentiment Analysis of Text Data with NLTK.” Journal of Physics: Conference Series, IOP Publishing, vol …

A little goes a long way: Improving toxic language classification despite data scarcity
M Juuti, T Gröndahl, A Flanagan, N Asokan – arXiv preprint arXiv …, 2020 – arxiv.org
… Table 1: Document lengths (number of sentences; tok- enized with NLTK sent tokenize (Bird et al., 2009)) … We lemmatized and annotated a large cor- pus with NLTK (Bird et al., 2009), and mapped each <lemma, tag> combination to its most com- mon surface form …

Categorizing Multilingual Customer Feedback
V Jain, M Kambli – researchgate.net
… NLTK’s default ‘WordNetLemmatizer’ was used for this process … Motivated by the recent developments in Transfer Learning (TL) where pre-trained models are used as starting point in natural language generation and classification tasks, we compared the results produced by …

Homographic pun location using multi-dimensional semantic relationships
Y Diao, H Lin, L Yang, X Fan, D Wu, K Xu – Soft Computing, 2020 – Springer
… For example, ”Boyle said he was under too much pressure.” Here, this sentence is a homographic pun and its pun word is ”pressure” which is a noun. We can use the POS tagger of NLTK to analyze the text and obtain the candidate pun words …

Towards Effective and Controllable Neural Text Generation
L Fang – 2020 – search.proquest.com
… emerges to be reusing BERT as model base, and then fine-tuning accordingly on task specific supervised data, as depicted in Figure 2.8. In terms of natural language generation tasks, the GPT models attract huge attention and serve as natural starting points for pre-trained …

Measuring Narrative Fluency by Analyzing Dynamic Interaction Networks in Textual Narratives.
OJ Lee, JT Kim – Text2Story@ ECIR, 2020 – ceur-ws.org
… nouns and pronouns, which are tagged as ‘NN,’ ‘NNS,’ ‘NNP,’ or ‘NNPS’ by the POS tagger in the NLTK library … In Proceedings of the Workshop on Computational Creativity in Natural Language Generation (CC-NLG@INLG 2017), pages 38–43, Santiago de Compostela, Spain …

CALLIG: Computer Assisted Language Learning using Improvisation Games
LM da Costa, JUS Sio – Workshop on Games and Natural Language …, 2020 – aclweb.org
Page 1. Proceedings of the LREC 2020 Workshop Games and Natural Language Processing, pages 49–58 Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020 c European Language …

An AI based Chatbot to Self-Learn and Self-Assess Performance in Ordinary Level Chemistry
A Mahroof, V Gamage, K Rajendran… – … on Advancements in …, 2020 – ieeexplore.ieee.org
… conducted in Indian Institute of Management Ahmedabad on auto generating questions for Tamil sentences [13] using Natural Language Generation (NLG) concept of … Preprocessing of the data is done using NLTK library which is a platform for building programs to work with …

Corpora and Baselines for Humour Recognition in Portuguese
HG Oliveira, A Clemêncio, A Alves – Proceedings of The 12th Language …, 2020 – aclweb.org
… Some (eg, incongruity, out-of-vocabulary words) are alternative applications of the exploited language resources. For extracting these features, pre-processing was first per- formed with Python’s NLTK, improved for Portuguese (Fer- reira et al., 2019) …

Automated Text Generation Driven By Data
MSPDF Khosmood – marshallplan.at
Page 1. Klimashevskaia Anastasiia, Bsc Automated Text Generation Driven By Data Marshall Plan Scientific Report submitted to Austrian Marshall Plan Foundation Supervisor Assoc.Prof. Dipl.-Ing. Dr.techn. Christian Gütl Institute of Interactive Systems and Data Science …

Converting the Point of View of Messages Spoken to Virtual Assistants
IG Lee, V Zu, SS Buddi, D Liang… – arXiv preprint arXiv …, 2020 – arxiv.org
… encoder-decoder approaches, which learn all necessary transformations directly from the data and which can perform natural language generation with some … BLEU is calculated using the corpus bleu method from the nltk.translate package, and METEOR is calcu- lated by …

Practical Natural
M Sri – Springer
… 177 Natural Language Generation in Banks ….. 177 … I explore techniques to build bots using state-of-the-art neural network architectures. This chapter also introduces natural language generation concepts. Page 13. 1 …

On the Summarization and Evaluation of Long Documents
A Gaskell – 2020 – imperial.ac.uk
Page 1. IMPERIAL COLLEGE LONDON DEPARTMENT OF COMPUTING On the Summarization and Evaluation of Long Documents Author: Alexander Gaskell Internal Supervisors: Dr. Pedro Baiz Prof. Lucia Specia External Supervisors: Hugo Barbaroux Dr. Eric Topham …

Text synthesis from keywords: a comparison of recurrent-neural-network-based architectures and hybrid approaches
N Kolokas, A Drosou, D Tzovaras – Neural Computing and Applications, 2020 – Springer
… 2, 22]. The encoder and the decoder typically correspond to consecutively applied RNNs. Such architecture has been applied for machine translation [23,24,25,26,27, 28,29,30] and natural language generation [31]. For the question …

Automatic paper writing based on a RNN and the TextRank algorithm
HC Wang, WC Hsiao, SH Chang – Applied Soft Computing, 2020 – Elsevier
… However, the rewritten abstracts require techniques such as information extraction, discourse understanding, and natural language generation … Due to advances in Seq2Seq and natural language generation techniques, it is possible to generate reasonable text …

A Socially-Aware Conversational Recommender System for Personalized Recipe Recommendations
F Pecune, L Callebert, S Marsella – Proceedings of the 8th International …, 2020 – dl.acm.org
… 2) a Dialog Manager (DM), deciding what to say next based on the output of the NLU and 3) a Natural Language Generation (NLG) module … Our NLU module uses the Python libraries nltk and Spacy to do lemmatization, dependency parsing and POS tagging on the ut- terance …

Text Simplification: From Daedalian to Simple
N Prabhu – 2020 – web2py.iiit.ac.in
… This thesis touches upon various topics along the vast expanse of text simplification research, starting from generic improvements to encoder-decoder mod- els that are used for controlled Natural Language Generation (NLG) tasks (including text simplifica- tion), to domain …

Table-to-Text: Generating Descriptive Text for Scientific Tables from Randomized Controlled Trials
Q Wei – 2020 – digitalcommons.library.tmc.edu
… generation) have received great attention, as natural language is the primary communication channel for human beings. Success stories applying natural language generation (NLG) techniques to produce meaningful textual description of real world events have been reported …

A STUDY ON PYTHON PROGRAMMING LANGUAGE
D Lakshminarayanan, S Prabhakaran – drsrjournal.com
… sentiment analysis of reviews of movie (BiswaRanjan, S. &Mrutyunjaya, P., 2017). Researchers were collected datasets of different sizes from movie reviews and tools used for processing the movie reviews is Python’s Natural Language Toolkit (NLTK) package along …

Developing Emotion-Aware Human–Robot Dialogues for Domain-Specific and Goal-Oriented Tasks
JY Huang, WP Lee, CC Chen, BW Dong – Robotics, 2020 – mdpi.com
… 30]. After the above sentence segmentation, we employed the Natural Language Processing Toolkit (NLTK, [31]) to build a dictionary, consisting of more than 7000 words, of which the most frequent stop words were removed …

Design and Implementation of a Chatbot for Kurdish Language Speakers Using Chatfuel Platform
HK Ahmed, JA Hussein – Kurdistan Journal of Applied Research, 2020 – kjar.spu.edu.iq
… Natural language generation, This includes using a database to obtain semantic goals and convert them into human language … 1) Parsing: it is used to analyze and process the input from users by using several functions of NLP, such as Python NLTK tree [47] …

Customized Impression Prediction from Radiology Reports Using BERT and LSTMs
B Gundogdu – researchgate.net
… seq2seq and Natural Language Generation techniques are now making it possible to generate reasonable summaries by using Abstraction [15], [16 … Still, in our exper- iments, following several word tokenization methods including Stanford CoreNLP, NLTK, Spacy and keras, we …

Chinese Grammatical Errors Diagnosis System Based on BERT at NLPTEA-2020 CGED Shared Task
H Zan, Y Han, H Huang, Y Yan, Y Wang… – Proceedings of the 6th …, 2020 – aclweb.org
… The output of the model is shown in Table 4: n-gram model: We extracted 400,490 3- gram combinations from 20,000 correct sentences through the NLTK tool … In Proceedings of the 13th European Workshop on Natural Language Generation.pp242-249 …

Do “Undocumented Immigrants”==“Illegal Aliens”? Differentiating Denotation and Connotation in Vector Space
A Webson, Z Chen, C Eickhoff, E Pavlick – Proceedings of the 2020 …, 2020 – aclweb.org
Page 1. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 4090–4105, November 16–20, 2020. c 2020 Association for Computational Linguistics 4090 Do “Undocumented Workers” == “Illegal Aliens” …

A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models
U Naseem, I Razzak, SK Khan, M Prasad – arXiv preprint arXiv …, 2020 – arxiv.org
… them. There are different stop-word libraries available such as NLTK, scikit-learn and spaCy. • Stemming … technique. Few of the famous ones are NLTK (Wordnet lemmatizer), genism, Stanford CoreNLP, spaCy and TextBlob etc. • Part …

TED-Q: TED Talks and the Questions they Evoke
M Westera, L Mayol, H Rohde – … of The 12th Language Resources and …, 2020 – aclweb.org
… Elicitation phase For the elicitation phase, texts were cut up into sentences (using NLTK’s sentence tokenizer), and long sentences only (> 150 words) were further cut up at commas, colons or semicolons by a simple script.2 For con- venience we will refer to the resulting pieces …

Weakly-Supervised Text Classification Using Label Names Only
Y Meng, Y Zhang, J Huang, C Xiong, H Ji… – Proceedings of the …, 2020 – aclweb.org
… Finally, we form the cat- egory vocabulary of each class using the top-100 words ranked by how many times they can replace the label name in the corpus, discarding stopwords with NLTK (Bird et al., 2009) and words that ap- pear in multiple categories …

Hybrid Words Representation for the classification of low quality text
U Naseem – 2020 – opus.lib.uts.edu.au
… standing (NLU) which empowers computers to understand the meaning from input received from natural language or humans and involves natural language generation (Pang and Lee; 2008a). Predefined labels are assigned in the text classification task …

To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging
K Bhattacharjee, M Ballesteros, R Anubhai… – arXiv preprint arXiv …, 2020 – arxiv.org
… 2019. BART: Denoising Sequence-to-Sequence pre- training for Natural Language Generation, transla- tion, and comprehension. Xiaoya Li, Xiaofei Sun, Yuxian Meng, Junjun Liang, Fei Wu, and Jiwei Li. 2019. Dice Loss for Data- imbalanced NLP Tasks …

The impact of distributed neural machine translation in sentiment analysis
? ?????? – 2020 – dione.lib.unipi.gr
… 19 1.3.2 Natural Language Generation (NLG) …. 21 … ?he term of NLP involves two different procedures: Natural Language Understanding (NLU) and Natural Language Generation (NLG) …

ProSOUL: a framework to identify propaganda from online Urdu content
S Kausar, B Tahir, MA Mehmood – IEEE Access, 2020 – ieeexplore.ieee.org
… detection. I. INTRODUCTION Recent developments in artificial intelligence, big data, and natural language generation are a double-edged sword … language. The library of Natural Language ToolKit (NLTK) [66] is used for stopword removal …

Building A User-Centric and Content-Driven Socialbot
H Fang – arXiv preprint arXiv:2005.02623, 2020 – arxiv.org
Page 1. ©Copyright 2019 Hao Fang arXiv:2005.02623v1 [cs.CL] 6 May 2020 Page 2. Building A User-Centric and Content-Driven Socialbot Hao Fang A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy …

Machine learning for learner English: A plea for creating learner data challenges
N Ballier, S Canu, C Petitjean, G Gasso… – … Journal of Learner …, 2020 – jbe-platform.com
Abstract This paper discusses machine learning techniques for the prediction of Common European Framework of Reference (CEFR) levels in a learner corpus. We summarise the CAp 2018 Machine Learning (ML) competition, a classification task of the six CEFR levels, which map …

A Deep Dive into Supervised Extractive and Abstractive Summarization from Text
M Dey, D Das – Data Visualization and Knowledge Engineering, 2020 – Springer
… One of the most important and challenging problem in case of abstractive summarization is natural language generation … 10.1.1 Preprocessing. Every sentence in each document is tokenized, stemmed using porter stemmer of nltk, later they are lemmatized …

Text classification using label names only: A language model self-training approach
Y Meng, Y Zhang, J Huang, C Xiong, H Ji… – arXiv preprint arXiv …, 2020 – arxiv.org
… Finally, we form the cat- egory vocabulary of each class using the top-100 words ranked by how many times they can replace the label name in the corpus, discarding stopwords with NLTK (Bird et al., 2009) and words that ap- pear in multiple categories …

Modelling speaker adaptation in second language learner dialogue
AJ Sinclair – 2020 – era.ed.ac.uk
Page 1. This thesis has been submitted in fulfilment of the requirements for a postgraduate degree (eg PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following terms and conditions of use: This work …

Transfer Learning for Abstractive Summarization at Controllable Budgets
R Sarkhel, M Keymanesh, A Nandi… – arXiv preprint arXiv …, 2020 – arxiv.org
… However, these methods cannot construct sum- maries at specified budgets. This has been recently addressed by Saito et al. [29]. They were able to generate abstractive summaries at specified lengths following a prototype-driven natural language generation approach [12] …

Unified Test Framework for Voice Enabled Devices
C Pradhan, SA Kinange… – 2020 IEEE 17th India …, 2020 – ieeexplore.ieee.org
… all voice assistant components (Automatic Speech Recognition -ASR, Natural Language Understanding -NLU, Natural Language Generation – NLG, wakeup) and … GRC 2007) 2007 IEEE [8] Mykhailo Lobur, Andriy Romanyuk, Mariana Romanyshyn, “Using NLTK for educational …

(Re) lexicalization of auto-written news with contextual and cross-lingual word embeddings
M Rämö – 2020 – helda.helsinki.fi
… In automated journalism, or robojournalism, some data is transformed into human read- able news reports with natural language generation techniques introduced earlier. Graefe … described earlier in Section 4.1 and POS-tagging is done with the NLTK library [3] …

Detection of Offensive Language in Social Media Posts
S Mehra – 2020 – researchgate.net
… is the art of understanding (Natural Language Understanding) and generating (Natural Language Generation) the human language such as text or speech by the … into Natural Language Understanding and Natural Language Generation which can be …

Efficient text summarization method for blind people using text mining techniques
S Basheer, M Anbarasi, DG Sakshi… – International Journal of …, 2020 – Springer
… Data driven approach used in sentence extraction is much easier compared to inference and natural language generation which is a bit more difficult to execute. There is in general no completely abstract description system today (Gillick et al. 2008; Gillick and Favre 2009) …

Influencing an artificial conversational entity by information fusion
J Salamon – 2020 – otik.uk.zcu.cz
… 135, 140 NLG Natural Language Generation. vi, viii, xvii, xviii, 9, 10, 17, 20, 51, 53, 54, 62, 66, 67, 78, 79, 86, 92, 106–108, 111, 112, 122, 124, 126, 135, 138, 140, 150, 152, 153 … NLTK Natural Language Toolkit. 35, 68 NLU Natural Language Understanding …

LEVERAGING DEPENDENCY STRUCTURE FOR INFERENCE COMPUTATION, SUMMARIZATION, AND COMPREHENSION
EJ GEORGE – 2020 – web2py.iiit.ac.in
Page 1. LEVERAGING DEPENDENCY STRUCTURE FOR INFERENCE COMPUTATION, SUMMARIZATION, AND COMPREHENSION Thesis submitted in partial fulfillment of the requirements for the degree of Master of Science …

Analyzing Neural Discourse Coherence Models
Y Farag, J Valvoda, H Yannakoudakis… – arXiv preprint arXiv …, 2020 – arxiv.org
… Since I wasn’t looking at my feet I stepped on a rock.’ There are also other coreferential de- vices such as: demonstrative references (eg, ‘this’ 6We use NLTK for word tokenization; sentence bound- aries are already marked in the stories …

A Robot System for Personalized Language Education. Implementation and evaluation of a language education system built on a robot
YC Dündar – 2020 – munin.uit.no
Page 1. Department of Computer Science A Robot System for Personalized Language Education Implementation and evaluation of a language education system built on a robot Yigit Can Dundar Master’s thesis in Computer Science…INF-3990-1…June 2020 Page 2. Abstract …

SentenceMIM: A Latent Variable Language Model
M Livne, K Swersky, DJ Fleet – arXiv preprint arXiv:2003.02645, 2020 – arxiv.org
Page 1. SentenceMIM: A Latent Variable Language Model Micha Livne 1 2 Kevin Swersky 3 David J. Fleet 1 2 Abstract We introduce sentenceMIM, a probabilistic auto- encoder for language modelling, trained with Mu- tual Information Machine (MIM) learning …

Relation Identification Using Dialogical Features in Multi-Party Conversation
T Himeno, K Shimada – pluto.ai.kyutech.ac.jp
… As the BOW features, we use all words without the stopword list by NLTK. As the word embedding, we use word2vec (W2V) 2 published by Google … In Proceedings of the 14th European Workshop on Natural Language Generation, pages 136–146, Sofia, Bulgaria, August …

Abstractive multi-document summarization-paraphrasing and compressing with neural networks
EO Egonmwan – 2020 – opus.uleth.ca
Page 1. ABSTRACTIVE MULTI-DOCUMENT SUMMARIZATION – PARAPHRASING AND COMPRESSING WITH NEURAL NETWORKS ELOZINO OFUALAGBA EGONMWAN Bachelor of Science, University of Benin, 2010 Master of Science, Johannes Kepler University, 2015 …

Data-Efficient Methods for Dialogue Systems
I Shalyminov – arXiv preprint arXiv:2012.02929, 2020 – arxiv.org
… MSE Mean Squared Error NER Named Entity Recognition NLG Natural Language Generation NLL Negative Log-Likelihood NLP Natural Language Processing NLTK Natural Language Toolkit NLU Natural Language Understanding NMT Neural Machine Translation …

Fine-Tuning Pre-Trained Language Models for German Multi-Document Summarization
T Johner – inf.uni-hamburg.de
Page 1. MASTER THESIS Fine-Tuning Pre-Trained Language Models for German Multi-Document Summarization vorgelegt von Timo Johner MIN-Fakultät Fachbereich Informatik Studiengang: IT-Management und -Consulting Matrikelnummer: 7213739 …

Deep mining of open source software bug repositories
A Hamdy, G Ezzat – International Journal of Computers and …, 2020 – Taylor & Francis
Large scale software projects adopt bug tracking systems such as Bugzilla and Jira to manage the bugs’ fixes and store their information. Mining bug repositories is essential to automate some maint…

Parsing an American Sign Language Corpus with Combinatory Categorial Grammar
MA Nix – 2020 – search.proquest.com
… also a brief mention of the types of parsers that were used4. 4The Natural Language Toolkit (NLTK) package for Python includes many of the parsers discussed. Bird et al. (2009) is a great resource for learning how to use NLTK. https://www.nltk.org/book …

Unsupervised Opinion Summarization with Content Planning
RK Amplayo, S Angelidis, M Lapata – arXiv preprint arXiv:2012.07808, 2020 – arxiv.org
Page 1. Unsupervised Opinion Summarization with Content Planning Reinald Kim Amplayo, Stefanos Angelidis, Mirella Lapata Institute for Language, Cognition and Computation School of Informatics, University of Edinburgh …

A semantic-aware video auto-captioning method
LT Tin – 2020 – eprints.utar.edu.my
… associate includes: NN – noun, singular, NNS – noun, plural, VB – verb, VBZ – verb, present tense and so on. The POS tagger is referenced from NLTK toolkit by (Anon., 2019). Figure 2.2 Other High-Level Features that are not related to human but analysed from …

On the naturalness of hardware descriptions
J Lee, P Nie, JJ Li, M Gligoric – Proceedings of the 28th ACM Joint …, 2020 – dl.acm.org
Page 1. On the Naturalness of Hardware Descriptions Jaeseong Lee? UT Austin (USA) jason.lee27@utexas.edu Pengyu Nie? UT Austin (USA) pynie@utexas.edu Junyi Jessy Li UT Austin (USA) jessy@austin.utexas.edu Milos Gligoric UT Austin (USA) gligoric@utexas.edu …

Composing Answer from Multi-spans for Reading Comprehension
Z Zhang, Y Zhang, H Zhao, X Zhou, X Zhou – arXiv preprint arXiv …, 2020 – arxiv.org
… We follow the self-attentive parser (Kitaev and Klein 2018) for giving syntactic constituency parse trees of sentences.8 NLTK sentence tokenization tool is used to split the given passage into several sentences.9 The average number of candidate spans of each question is 18 …

Sentiment Analysis Machine Learning & Deep Learning based approach
S eddine Nacer – 2020 – archives.univ-biskra.dz
… 48 5.2.2.5 NLTK … essentially can be classified into two main parts ie Natural Language Understanding and Natural Language Generation that is used as the assignment or task to understand and produce the content or in other words generate the content …

Towards Open-Ended VQA Models Using Transformers
AM Bellini – 2020 – indigo.uic.edu
… NLG Natural Language Generation OOV Out Of Vocabulary BPE Byte Pair Encoding … NLP is a vast area of research that consists of many sub-fields, such as Natural Language Understanding, Natural Language Generation, and down-stream tasks from Speech Recogni- tion …

Integrating Latent Dirlichet Allocation to Centroid-based Text Summarization
L Ralston, J Berry – logan-r.com
… focused, due to extractive-based methods avoiding the difficulties of emulating human language capabilities present in natural language generation – and, in … data text, we implement our own preprocessor mini-module for text cleaning, including classes based on either nltk1 or …

Chatbot for food preferences modelling and recipe recommendation
ÁMFM Samagaio – 2020 – repositorio-aberto.up.pt
… 90 5.2 Preference Modelling . . . . . 92 5.2.1 VADER NLTK Sentiment Analyser . . . . . 93 5.2.2 Google Cloud Natural Language API . . . . . 94 5.2.3 Sentiment Analysis for preference modelling …

Counter-Masquerading: A Logicist-AI Approach to Interventionist Strategies
R Ghosh – 2020 – search.proquest.com
Page 1. COUNTER-MASQUERADING: A LOGICIST-AI APPROACH TO INTERVENTIONIST STRATEGIES Rikhiya Ghosh Submitted in Partial Fullfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY Approved …

Are” Undocumented Workers” the Same as” Illegal Aliens”? Disentangling Denotation and Connotation in Vector Spaces
A Webson, Z Chen, C Eickhoff, E Pavlick – arXiv preprint arXiv:2010.02976, 2020 – arxiv.org
Page 1. Are “Undocumented Workers” the Same as “Illegal Aliens”? Disentangling Denotation and Connotation in Vector Spaces Albert Webson1,2, Zhizhong Chen3, Carsten Eickhoff1, and Ellie Pavlick1 {albert webson, zhizhong …

Noetic end-to-end response selection with supervised neural network based classifiers and unsupervised similarity models
P Skórzewski, W Siei?ska, M Kubis – Computer Speech & Language, 2020 – Elsevier
… systems is to build a pipeline of separate modules for natural language understanding, dialogue state tracking, action selection and natural language generation … The second processor is a lemmatizer, which is a WordNetLemmatizer from the NLTK toolkit (Bird et al., 2009) …

Improving the robustness of machine reading comprehension model with hierarchical knowledge and auxiliary unanswerability prediction
Z Wu, H Xu – Knowledge-Based Systems, 2020 – Elsevier
… They performed multi-task learning with paragraph ranker and answer possibility classifier to improve their natural language generation performance. Different from this work, we improve the robustness of our extractive RC model with auxiliary unanswerability prediction …

Interpretability for Deep Learning Text Classifiers
D Lucaci – 2020 – ruor.uottawa.ca
Page 1. Interpretability for Deep Learning Text Classifiers by Diana Lucaci Thesis submitted to the University of Ottawa in partial Fulfillment of the requirements for the MCS degree in Computer Science School of Electrical Engineering and Computer Science (EECS) …

Neural Information Extraction on Technical Short Text: From Theory to Practical Systems
M Stewart – 2020 – api.research-repository.uwa.edu.au
… 31 2.7.4.3 Stanford CoreNLP . . . . . 31 2.7.4.4 AllenNLP . . . . . 32 2.7.4.5 NLTK . . . . . 32 2.7.4.6 SpaCy . . . . . 32 2.8 Conclusion …

Cross-lingual entity extraction and linking for 300 languages
X Pan – 2020 – ideals.illinois.edu
… Kevin Knight. “Describing a Knowledge Base”. Proc. The 11th International Conference on Natural Language Generation. 6 Page 14. CHAPTER 2: SYMBOLIC SEMANTICS BASED ENTITY LINKING 2.1 APPROACH OVERVIEW A typical Entity Linking system works as follows …

Language learning in the age of Artificial Intelligence
K PRENGA – academia.edu
Page 1. UNIVERSITÀ DEGLI STUDI DI MILANO Facoltà di Studi Umanistici Corso di Laurea in Lingue e Letterature Europee ed Extraeuropee Language learning in the age of Artificial Intelligence Relatrice: Professoressa Barbara BERTI …

Implementing a Bot Assistant: A case study for a bot helping users using an app
C Kalleas – 2020 – repository.ihu.edu.gr
… 20 Page 6. -vi- Natural Language Toolkit (NLTK) …. 21 … dates, times and prices [36]. Page 21. -21- Natural Language Toolkit (NLTK) The Natural Language Toolkit (NLTK) is a set of tools based on the principles of NLP technology [33] …

Automatic Text Summarization of Patent Documents
E Gustafsson – 2020 – lup.lub.lu.se
… The challenge with abstraction-based methods is to handle semantic representa- tion, inference, and natural language generation which is computationally more taxing and often requires deep domain knowledge of the documents [2, 16] …

Semantic chunking
E Muszynska – 2020 – repository.cam.ac.uk
Page 1. Semantic chunking Ewa Muszy ´nska Supervisor: Prof. Ann Copestake Department of Computer Science and Technology University of Cambridge This dissertation is submitted for the degree of Doctor of Philosophy. Clare Hall October 2020 Page 2. Page 3. Declaration …

A multi-lingual and cross-domain analysis of features for text simplification
R Stodden, L Kallmeyer – Proceedings of the 1st Workshop on Tools and …, 2020 – aclweb.org
… (2018). In contrast to them, we are offer- ing the usage of SpaCy 9 and Stanza 10 instead of NLTK for pre-processing … We use 12 dif- ferent BLEU implementations, 8 from the Python package NLTK and 4 implemented in Sharma et al. (2017). 3.3 …

Next Word Prediction Based On De ep Learning
F LAHRACHE, S DJEBRIT – 2020 – dspace.univ-ghardaia.dz
… nltk Brown 65.20% 71.51% Kaggle GoogleColab 19 … In this project we use three databases: the first one is Coursera Swiftkey, the second is the book: Nietzsche Writings: Volume1 by Friedrich Nietzsche and the third is the News category from the Brown corpus in the nltk library …

Predictive Model: Using Text Mining for Determining Factors Leading to High-Scoring Answers in Stack Overflow
RQ Selleras – 2020 – search.proquest.com
… MIMIC Multiple Indicators and Multiple Causes NER Named Entity Recognition NIR Near-Infrared Ray NLP Natural Language Processing NLTK Natural Language Toolkit PLS Partial Least Squares SEDE Stack Exchange Data Explorer SEO Search Engine Optimization …

Deep learning and reinforcement learning methods for grounded goal-oriented dialogue
H de Vries – 2020 – papyrus.bib.umontreal.ca
Page 1. Université de Montréal Deep Learning and Reinforcement Learning Methods for Grounded Goal-Oriented Dialogue par Harm de Vries Département d’informatique et de recherche opérationnelle Faculté des arts et des sciences …

# covid-19 on twitter: Bots, conspiracies, and social media activism
E Ferrara – arXiv preprint arXiv:2004.09531, 2020 – arxiv.org
… have significantly improved: bots rely on the fast-paced advancements of Artificial Intelligence, especially in the area of natural language generation, and use … terms that include short function words, as well as non-lexical words, are also removed using the “nltk” Python library …

A Study on Learning Representations for Relations Between Words
H Hakami – 2020 – livrepository.liverpool.ac.uk
Page 1. A Study on Learning Representations for Relations Between Words Thesis submitted in accordance with the requirements of the University of Liverpool for the degree of Doctor in Philosophy in Faculty of Science and …

A Conversational Movie Recommender System
J Habib – 2020 – uis.brage.unit.no
… 12 2.2.1 Natural Language Understanding (NLU) . . . . 12 2.2.2 Natural Language Generation (NLG) … 27 3.2.3 Natural Language Understanding (NLU) . . . . 35 3.2.4 Natural Language Generation (NLG) …

Anomaly detection through explanations
LH Gilpin – 2020 – dspace.mit.edu
Page 1. Anomaly Detection Through Explanations by Leilani Hendrina Gilpin BS, University of California, San Diego (2011) MS, Stanford University (2013) Submitted to the Department of Electrical Engineering and Computer Science …

Iconic: Discerning Insightful Contexts in Image Captioning By Leveraging Commonsense Knowledge
MM Bundele – 2020 – utd-ir.tdl.org
Page 1. ICONIC: DISCERNING INSIGHTFUL CONTEXTS IN IMAGE CAPTIONING BY LEVERAGING COMMONSENSE KNOWLEDGE by Manas Bundele APPROVED BY SUPERVISORY COMMITTEE: Jessica Ouyang, Chair Dan Moldovan Vincent Ng Page 2 …

Automatic term extraction for conventional and extended term definitions across domains
A Hätty – 2020 – elib.uni-stuttgart.de
Page 1. Automatic Term Extraction for Conventional and Extended Term Definitions across Domains Von der Fakultät Informatik, Elektrotechnik und Informationstechnik der Universität Stuttgart zur Erlangung der Würde eines …

Surmize: An Online NLP System for Close-Domain Question-Answering and Summarization
A Bergkvist, N Hedberg, S Rollino, M Sagen – 2020 – diva-portal.org
Page 1. Sj¨alvst¨andigt arbete i informationsteknologi 8 juni 2020 Surmize: An Online NLP System for Close-Domain Question-Answering and Summarization Alexander Bergkvist, Nils Hedberg, Sebastian Rollino, Markus Sagen …

A Study of Information Bots and Knowledge Bots
A Hatua – 2020 – aquila.usm.edu
… Warping CVI – Cluster Validity Index ARIMA – Autoregressive Moving Averag LSTM – Long Short-Term Memory RNN – Recurrent Neural Network RMSE – Root Mean Square Error NLU – Natural Language Understanding NLG – Natural Language Generation RL – Reinforcement …

CORPUS EXPLORATION AND DIALOGUE SYSTEM DESIGN FOR A VIRTUAL LIBRARIAN
X Li – 2020 – gupea.ub.gu.se
… contain a series of modules: automatic speech recognition (ASR), natural language understanding (NLU), dialogue management, natural language generation (NLG), and … 14 The analysis is carried out with python and NLTK library (an alphabetically ordered tag dictionary see …

Artificial intelligence for social good: A survey
ZR Shi, C Wang, F Fang – arXiv preprint arXiv:2001.01818, 2020 – arxiv.org
… and techniques for which the title or abstract contained a keyword corresponding to those domains/techniques, making use of nltk.stem … For example, Wang and Su propose to use natural language generation aided with dimensional units to generate math exercise problems [45 …

Hybrid machine learning architecture for phishing email classification
K Koutroumpouchos – 2020 – dione.lib.unipi.gr
Page 1. UNIVERSITY OF PIRAEUS School of Information and Communication Technologies Department of Digital Systems Hybrid Machine Learning Architecture for Phishing Email Classification M.Sc. Digital Systems Security Thesis Author: Koutroumpouchos Konstantinos …

What types of covid-19 conspiracies are populated by twitter bots?
E Ferrara – First Monday, 2020 – journals.uic.edu
… have significantly improved: bots rely on the fast-paced advancements of Artificial Intelligence, especially in the area of natural language generation, and use … terms that include short function words, as well as non-lexical words, are also removed using the “nltk” Python library …

Deep Generative Models for Semantic Text Hashing
S Chaidaroon – 2020 – scholarcommons.scu.edu
Page 1. Santa Clara University Scholar Commons Engineering Ph.D. Theses Student Scholarship 3-2020 Deep Generative Models for Semantic Text Hashing Suthee Chaidaroon Follow this and additional works at: https://scholarcommons.scu.edu/eng_phd_theses …

Improving document ranking with query expansion based on bert word embeddings
D Yeke – 2020 – open.metu.edu.tr
Page 1. IMPROVING DOCUMENT RANKING WITH QUERY EXPANSION BASED ON BERT WORD EMBEDDINGS A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES OF MIDDLE EAST TECHNICAL UNIVERSITY BY …

Towards Subjective Multimedia Summarization Framework for Sporting Event in the Context of Digital Twins
SB Aloufi – 2020 – ruor.uottawa.ca
Page 1. Towards Subjective Multimedia Summarization Framework for Sporting Event in the Context of Digital Twins by Samah Bader Aloufi Thesis submitted in partial fulfillment of the requirements For the Doctorate in Philosophy degree in Computer Science …

A Companion Robot for Modeling the Expressive Behavior of Persons with Parkinson’s Disease
AP Valenti – 2020 – search.proquest.com
Page 1. A Companion Robot for Modeling the Expressive Behavior of Persons with Parkinson’s Disease A dissertation submitted by Andrew P. Valenti, BS, MS, New York University In partial fulfillment of the requirements for the degree of Doctor of Philosophy in …