Skipgram & Chatbots 2019


Notes:

The continuous Skip-gram algorithm is an efficient deep learning method for learning high-quality distributed vector representations that capture a large number of precise semantic word relationships.

Resources:

  • bioasq .. organizes challenges on biomedical semantic indexing and question answering
  • computational stylistics group .. suite of stylometric tools
  • ijcai.org .. international joint conference on artificial intelligence (melbourne, august 2017)
  • movieqa .. dataset which aims to evaluate automatic story comprehension

Wikipedia:

See also:

100 Best Word2vec VideosConcGramsWord2vec & Chatbots 2018


CAISY: Chatbot using Artificial Intelligence and Sequential Model with YAML Dataset
P JM, S Kashyap, PR Nargund – 2019 – papers.ssrn.com
… Between the 80s – 90s numerous chatbots were developed – some predominant ones are ALICE, Dr.Sbaitso[9]. Later in the start of the 21st century, ie, in the year 2001 … From the paper, we can infer that skip gram model is suitable for the design of the Chatbot and also we can …

Building an enterprise chatbot: Work with protected enterprise data using open source frameworks
A Singh, K Ramasubramanian, S Shivam – 2019 – books.google.com
… Page 19. INTRODUCTIONINTRODUCTION • Learn how to deploy a complete in-house-built chatbot using an open source technology stack like RASA and Botpress (such chatbots avoid sharing any PIIs with any third-party tools) • Develop a chatbot called IRIS from scratch by …

Building an Enterprise Chatbot
A Singh, K Ramasubramanian, S Shivam – Springer
… Natural language understanding, processing, and generation Page 19. xxii • Learn how to deploy a complete in-house-built chatbot using an open source technology stack like RASA and Botpress (such chatbots avoid sharing any PIIs with any third-party tools) …

INTROSPECTION OF NATURAL LANGUAGE PROCESSING FOR AI CHATBOT
S Kulthe, MV Tiwari, MM Nirmal, B Chaudhari – ijtre.com
… Recently, Mikolov et al.[3] introduced the Skip-gram model, an efficient method for learning high … generate some random out of context responses.[4] After ELIZA various Chatbots were build … implemented using C and C++ led to evolution of further chatbot implementation using …

An Intelligent Question and Answering System for Dental Healthcare
Y Jiang, Y Xu, J Guo, Y Liu, R Li – International Conference on Broadband …, 2019 – Springer
… This paper uses the skip-gram model in word2vec.The Q&A system mainly consists of two modules: semantic analysis and retrieval … The retrieval mainly relates to technologies such as indexing and search. The Chatbots API calls the API provided by Turing Robotics …

Word Embeddings-Skip Gram Model
PP Krishna, A Sharada – International Conference on Intelligent …, 2019 – Springer
… Few applications of word embedding are like machine translation, sentiment analysis, named entity recognition, chat bots and so on. 1.2 Skip Gram Model. Skip gram model aims to predict the context words for given input word …

# MeTooMaastricht: Building a chatbot to assist survivors of sexual harassment
T Bauer, E Devrim, M Glazunov, WL Jaramillo… – … Conference on Machine …, 2019 – Springer
… On the other hand, DBOW is similar to skip-gram that tries to predict randomly sampled words from the paragraph as outputs … 2.4 Chatbots. Chatbot technology was firstly introduced with the implementation of ELIZA in 1964 …

SSE: Semantic Sentence Embedding for learning user interactions
JF Lilian, K Sundarakantham… – 2019 10th …, 2019 – ieeexplore.ieee.org
… Wu and Li [11] proposed a model which would include the topic information for retrieval based chatbots.It models a … Word2Vec using SkipGram model (SG … these words are obtained using our proposed three layer network model which is similar to the word2vec skip gram model …

Short Text Conversation Based on Deep Neural Network and Analysis on Evaluation Measures
HE Cherng, CH Chang – arXiv preprint arXiv:1907.03070, 2019 – arxiv.org
… methods that evaluate the quality and structure of dialogue between a chatbot and a … With such measures, the quality of chatbots could be evaluated automatically and efficiently without … representation, embedding layer and utterance layer, one is based on skip-gram with multi …

Natural Language Processing and Conversational Shopping
L Luce – Artificial Intelligence for Fashion, 2019 – Springer
… Skip Grams. One way of analyzing these relationships and their proximity is by using skip grams … Figure 2-4 Word pairs from a skip gram training model. Part-of-Speech Tagging … Chatterbots— Another name for a chatbot, chatterbot is no longer popular terminology …

Chatbots with Personality Using Deep Learning
S Gaikwad – 2019 – scholarworks.sjsu.edu
… According to the authors, CBOW is faster while skip-gram is slower but does a better job for infrequent words … The generated word vectors will then be used for training the chatbot encoder-decoder model. Page 32. CHATBOTS WITH PERSONALITY USING DEEP LEARNING …

Typographic-Based Data Augmentation to Improve a Question Retrieval in Short Dialogue System
HS Nugraha, S Suyanto – 2019 International Seminar on …, 2019 – ieeexplore.ieee.org
… For example, in a data set collected from a social media-based Indonesian chatbot, two sentences: “Apakah web Telkom University sedang … in [8] propose two efficient neural network models for word-embedding learning, ie Continuous Bag-of-Words (CBoW) and Skip-gram …

Anniversary article: Then and now: 25 years of progress in natural language engineering
J Tait, Y Wilks – Natural Language Engineering, 2019 – search.proquest.com
… In particular, the move to the use of n-grams or skip grams and/or chunking with part of speech tagging and away from whole … Another is chatbot technology … Third, the widespread use of word n-gram, skip-gram and related models at the expense of deeper structures is striking …

Intention Classification Based on Transfer Learning: A Case Study on Insurance Data
S Tang, Q Liu, W Tan – International Conference on Human Centered …, 2019 – Springer
… Therefore, it is a very complicated work to develop intelligent chatbot for insurance industry … And they propose an approach based on the skipgram model to solve this problem, which can train models on large corpora quickly and allow to compute word representations for words …

Pun generation with surprise
H He, N Peng, P Liang – arXiv preprint arXiv:1904.06828, 2019 – arxiv.org
… the ratio of probabilities under a language model, and (ii) a retrieve-and-edit approach based on words suggested by a skip-gram model … al., 2016), story gen- eration (Meehan, 1977; Peng et al., 2018; Fan et al., 2018; Yao et al., 2019), and social chat- bots (Weizenbaum, 1966 …

A Survey on Evaluation Methods for Chatbots
W Maroengsit, T Piyakulpinyo, K Phonyiam… – Proceedings of the …, 2019 – dl.acm.org
… the number of overlap word using N-gram, word sequence, or skip- grams etc … of ROUGE which are ROUGE-1 (Unigram), ROUGE-2 (Bi-gram), and ROUGE-SP4 (Skip-gram) … chatbot research articles focusing especially on architecture and evaluation techniques of each chatbot …

Bot2Vec: Learning Representations of Chatbots
J Herzig, T Sandbank, M Shmueli-Scheuer… – Proceedings of the …, 2019 – aclweb.org
… As conversational systems (ie, chatbots) become more pervasive, careful analysis of their … 1https://www.techemergence.com/chatbot-comparison- facebook-microsoft-amazon-google … In the context of learning word representations using the Word2Vec skip-gram model (Mikolov …

Automatic Ontology Population Using Deep Learning for Triple Extraction
MH Su, CH Wu, PC Shih – 2019 Asia-Pacific Signal and …, 2019 – ieeexplore.ieee.org
… Therefore, ontology is useful for a chatbot system [14]-[15]. However, constructing an ontology is very difficult and time- consuming … The word-level vector is trained by the Skip-Gram model of word2vec with the Gigaword database [25] …

A Survey on Evaluation Methods for Chatbots [Draft Version]
W Maroengsit, S Pongnumkul, T Piyakulpinyo… – researchgate.net
… the number of overlap word using N-gram, word sequence, or skip- grams etc … of ROUGE which are ROUGE-1 (Unigram), ROUGE-2 (Bi-gram), and ROUGE-SP4 (Skip-gram) … chatbot research articles focusing especially on architecture and evaluation techniques of each chatbot …

A Personal Conversation Assistant Based on Seq2seq with Word2vec Cognitive Map
M Shen – ?????????. ????????, 2019 – core.ac.uk
… This paper will describe how this data is collected, how to develop a personalized chatbot using personal conversation records … Word2vec is usually divided into CBOW (Continuous Bag- of-Words) and Skip-gram models depending on how it defines the input and output of data …

Enhancing Query Expansion Method Using Word Embedding
N Yusuf, MAM Yunus, N Wahid, N Wahid… – 2019 IEEE 9th …, 2019 – ieeexplore.ieee.org
… Datasets Query Expansion Methods UnigramBM25 with CBOW UnigramBM25 with Skip-gram Proposed UnigramBM25 with Glove YusufAli 0.2346 0.2336 0.2415 Arberry 0.2345 … [14] L. Hidayatin and F. Rahutomo, “Query Expansion Evaluation for Chatbot Application,” Proc …

Methods for compositional word embeddings learning
AT Sofronova – … -???????????????????? ?????????? ? …, 2019 – elibrary.ru
… 2. fips ml dataset; 3. russian journals ml dataset Figure 1. Architecture of Word2Vec models: CBOW and Skip-Gram After that … ago, and this has made it possible for developing numerous exciting applications for speech recognition, music synthesis, chatbots, machine translation …

Long Term Memory in Conversational Robots
J Olson, E Södergren – 2019 – diva-portal.org
… A chatbot with a long-term memory is likely to improve customer engagement [3], improve … Generally, chatbots seem to improve response speed in customer interactions [22] … Parameter Value Size 100 Window size 10 Method Continuous skipgram Vocabulary Size 3 010 472 …

Dialogue quality and nugget detection for short text conversation (STC-3) based on hierarchical multi-stack model with memory enhance structure
HE Cherng, CH Chang – NTCIR14. p. to appear, 2019 – research.nii.ac.jp
… to save a plenty of time and human resources, and provide a 24-hour chatbot to answer … is proposed in NTCIR-12 as the first step toward natural language conversation for chatbots … learning algorithm, which trains the vector of words from given corpus by skip-gram and CBOW …

Sémantické porozum?ní konverzaci
P Lorenc – 2019 – dspace.cvut.cz
… Each conversation has 20 turns on average. It sounds like very promising data for future development in chatbots’ field. Usually chatbot gets a query q and system returns a response r. Respond model has usually two options …

Deep Learning-based Categorical and Dimensional Emotion Recognition for Written and Spoken Text
BT Atmaja – 2019 – osf.io
… While the papers reported the use of GRU to build chat bot and to recognize categorical emotion, this paper proposed evaluation of GRU for both categorical and dimensional emotion with the same architecture … Fig. 1. Two architectures of word2vec: CBOW and Skip-gram [24] …

Natural language processing: opportunities and challenges for patients, providers, and hospital systems
CM Corcoran, C Benavides, G Cecchi – Psychiatric Annals, 2019 – healio.com
… approach constitutes natural language processing (NLP), which has increased the illusion of actual understanding by modern-day chatbots as compared … paying attention to word order (“continuous bag of words”), or with keeping the order but skipping over words (“skip grams”) …

Personal Trait Analysis Using Word2vec Based on User-Generated Text
G Sun, A Guo, J Ma, J Wei – … & Big Data Computing, Internet of …, 2019 – ieeexplore.ieee.org
… We can not only rely on the item that the user has purchased, but also recommend the product according to the user’s trait [1], [2]. A neural chatbot with personality is built by [3] which helps us better simulate a specific individual … The other one is called skip-gram …

Intelligent System of Personnel Management
VM Sineglazov – Electronics and control systems., 2019 – irbis-nbuv.gov.ua
… Finally, AI is used to create chat bots that can automate repetitive communication tasks … The most well-known methods for representing a word using a fixed-length vector are: one-hot encoding, Word2Vec [4], Continuous Bag of Words (CBOW), Skipgram (Skipgram model works …

Chatbot de Suporte para Plataforma de Marketing Multicanal
LASM Ferreira – 2019 – recipp.ipp.pt
… Para este efeito, foram desenvolvidos protótipos de várias frameworks para gestão de chatbots e de Natural Language … 13 2.2.8 Skip-gram Representation … This chatbot should be scalable to be multiplatform (able to communicate with the user through multiple channels) and …

Natural Language Processing, Understanding, and Generation
A Singh, K Ramasubramanian, S Shivam – Building an Enterprise Chatbot, 2019 – Springer
… Some chatbots are heavy on generative responses, and others are built for retrieving information … Since this book is about building an enterprise chatbot, we will focus more … Word embedding learnings: Provides many word embedding models using skipgram and Continous Bag …

An Analysis Tool for Spoken Language in VR
G Gilbert – idc.ac.il
… It has two training methodologies as well – PV -DBOW, which is skip-gram like, and Distributed Memory Model of Paragraph Vectors … 5. TickTock and IRIS from WOCHAT (Workshops and Session Series on Chatbots and Conversational Agents) (http://workshop.colips.org/ wochat …

Unsupervised dialogue intent detection via hierarchical topic model
A Popov, V Bulatov, D Polyudova… – Proceedings of the …, 2019 – aclweb.org
… Abstract One of the challenges during a task- oriented chatbot development is the scarce availability of the labeled training data … The most popular embedding models belong to the word2vec family (Mikolov et al., 2013b): CBOW, Skip-gram and their modifications (Mikolov et …

Generative Chat Bot Implementation Using Deep Recurrent Neural Networks and Natural Language Understanding
N Zalake, G Naik – … 2019: Conference on Technologies for Future …, 2019 – papers.ssrn.com
… Memory, Attention Mechanism, Beam Search, BLEU Score, Deep Learning, Bidirectional RNN, Chatbot, Generative bots … model that can be used to find embedding is Word2Vec skip-gram model which … C. Jain, A. Nagvenkar, and K. Modi, “Production Ready Chatbots: Generate if …

TKUIM at NTCIR-14 STC-3 CECG task
S Wei, C Cheng, Y Guang-zhong-yi Cao, CW Chiang… – research.nii.ac.jp
… Most chatbots are based on generative models, which can be improved under the Seq2Seq … In the following we will introduce several techniques related to the generative chatbot used in … There are two most commonly used word vector training methods: CBOW and Skip- Gram …

AI Affective Conversational Robot with Hybrid Generative-Based and Retrieval-Based Dialogue Models
MY Day, CS Hung – … on Information Reuse and Integration for …, 2019 – ieeexplore.ieee.org
… Model Building & Training ChatBot Mode Emotion Model Similarity Model … We employed the trained word2vec similarity model with skip-gram algorithm to calculate the weights of similarity and use the response with the highest weight in each sentiment group as the output …

Exploring the context of recurrent neural network based conversational agents
R Piccini, G Spanakis – arXiv preprint arXiv:1901.11462, 2019 – arxiv.org
… conversational agents, also known as Dialogue Systems, or more informally Chatbots, are becoming … including sta- tistical modeling of language (Manning et al., 1999), skip-gram models (Mikolov … This approach should make it simpler for the chatbot to extract informa- tion from …

Standardization of Robot Instruction Elements Based on Conditional Random Fields and Word Embedding
H Wang, Z Zhang, J Ren, T Liu – Journal of Harbin Institute of …, 2019 – hit.alljournals.cn
… have fixed procedure for information collection about destination, transportation, accommodation, etc., and also different from ordinary chatbots which usually … a prediction model using shallow neural network based on Continuous Bag-of-Words (CBOW) and Skip-gram and the …

Natural language processing recipes
A Kulkarni, A Shivananda – 2019 – Springer
… Advanced feature engineering techniques (word2vec and fastText) to capture context. • Information/Document Retrieval Systems, for example, search engine. • Chatbot, Q & A, and Voice-to-Text applications like Siri and Alexa. INTRODUCTION Page 23. xxv …

ACA: Attention-Based Context-Aware Answer Selection System
K Sundarakantham, JF Lilian, H Rajashree… – … Conference on Machine …, 2019 – Springer
… Abstract. The main goal of question answering system is to develop chatbots capable of answering the questions irrespective of the domain … He has developed two architectures, namely skip-gram model (SG) and continuous bag-of-words model (CBOW) …

Natural language processing
P Singh – Machine Learning with PySpark, 2019 – Springer
… There are many applications of NLP that are heavily used by businesses these days such as chatbot, speech recognition, language translation, recommender systems, spam detection, and sentiment analysis … There are two ways to calculate the embeddings. 1. Skip Gram. 2 …

Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction
W Wei, J Liu, X Mao, G Guo, F Zhu, P Zhou… – Proceedings of the 28th …, 2019 – dl.acm.org
… built for various pur- poses like emotional interaction, customer service or information acquisition, which can be roughly categorized into three classes, ie, chitchat chatbots, task-oriented chatbots and domain-specific chat- bots. For example, a task-specific chatbot can serve as …

Classification of user attitudes in Twitter-beginners guide to selected Machine Learning libraries
M Sokolowska, M Mazurek, M Majer, M Podpora – IFAC-PapersOnLine, 2019 – Elsevier
… It can also affect the im- provement of voice assistant systems and web-based chat- bots … The Continuous Bag of Words (CBOW) and Skip-gram, being an implementation of the Word2Vec model … be an issue of a significant importance for artificial agents, eg chatbots, because in …

Optimising user experience with: conversational Interfaces
AMG Costa – 2019 – recipp.ipp.pt
… can be specifically formatted if you are communicating with this chatbot via the Messenger … learning techniques are helping chatbots get closer where the customers will find it difficult to … two models, the Continuous Bag-of-Words model (CBOW) and the skip-gram model …

Subject Recognition in Chinese Sentences for Chatbots
F Li, H Wei, Q Hao, R Zeng, H Shao… – … Conference on Natural …, 2019 – Springer
… We use a traditional n-gram language model kenLM 2 , and use large-scale news corpora and chatbot dialogue corpus which contain approximately 100 … 4 Subject Recognition of Chatbots … We used the skip-gram architecture of Word2vec and the dimension is set to be 300 …

Recognition of emotions, valence and arousal in large-scale multi-domain text reviews
A Marchewka, A Czoska, D Grimling, B Konat… – researchgate.net
… We used the implementation of CBOW and Skip-gram methods provided with fastText tool (Bo … this work, which is EC1 (Kocon and Gawor, 2019) (kgr10.plain.skipgram.dim300.neg10 … of such research can be used in several applications – media monitoring, chatbots, stock prices …

Type of Response Selection utilizing User Utterance Word Sequence, LSTM and Multi-task Learning for Chat-like Spoken Dialog Systems
K Ohta, R Nishimura, N Kitaoka – 2019 Asia-Pacific Signal and …, 2019 – ieeexplore.ieee.org
… sations [3]. The primary aim of such non-task-oriented con- versation systems is for users to enjoy the conversation itself, thus it is more important for chatbots to be … We adopted a skip- gram model for training, and the number of dimensions of the representation was set to 200 …

Automated scoring of chatbot responses in conversational dialogue
SK Yuwono, B Wu, LF D’Haro – 9th International Workshop on Spoken …, 2019 – Springer
… can be obtained by training shallow networks such as continuous bag-of-words (CBOW) and skip-gram (SKIP) models … There are 11 datasets (from many different chatbots) released by WOCHAT … is defined as a pair of 2 turns, one by human, and another one by chatbot (the reply …

Hotel Review Sentiment Analysis using Natural Language Processing
A Lykesas – 2019 – ikee.lib.auth.gr
… google search that finds relevant and similar results), machine translation, speech recognition, text summarization and categorization, chat bots that are … The first approach is Continuous Bag-of-Words (CBOW) model and the second Skip-Gram model as shown in Figure 3.4 …

Expanding the Text Classification Toolbox with Cross-Lingual Embeddings
M M’hamdi, R West, A Hossmann, M Baeriswyl… – arXiv preprint arXiv …, 2019 – arxiv.org
… from different languages as de- scribed in Appendix C. The second type of em- beddings multi(skip gram) uses skip-gram ob- jective modified for multilingual setting as intro- duced by (Luong et al., 2015). 3.1.2 Multi-Filter CNN …

Evaluating Random Forest and a Long Short-Term Memory in Classifying a Given Sentence as a Question or Non-Question
F Ankaräng, F Waldner – 2019 – diva-portal.org
… method to compute W and therefore create word embeddings, is the Continuous Skip-gram Model … Weizenbaum in 1966.[12] Today there are two general ap- proaches to creating a chatbot. The first approach is the oldest of the two, where chatbots typically can be described as …

Using graph embeddings for Wikipedia link prediction
RV Shaptala, GD Kyselev – 2019 – repository.kpi.kharkov.ua
… DBPedia [5]. These technologies are at the core of a wide range of applications such as question answering, recommender systems and chatbots … link to each other or only one of them references another one – the link is created anyway); word-based skip-gram model, which …

FastText-Based Intent Detection for Inflected Languages
K Balodis, D Deksne – Information, 2019 – mdpi.com
… require a large amount of training data, which is not the typical case for chatbots … We trained a 300-dimensional skipgram model using the default fastText parameters (lr = 0.05 … The chatbot dataset contains users’ questions from a Telegram chatbot that answers questions related …

Sentence Similarity Computation in Question Answering Robot
S Si, W Zheng, L Zhou, M Zhang – Journal of Physics: Conference …, 2019 – iopscience.iop.org
… on its predecessors, as in traditional language models, CBOW predicts a word from its surrounding words or context and Skip-gram predicts multiple … The findings in this research are very useful to the development of chatbots in Zhiyan Technology (Shenzhen) Limited …

Deep Learning for Natural Language Processing: Solve your natural language processing problems with smart deep neural networks
KR Bokka, S Hora, T Jain, M Wambugu – 2019 – books.google.com
Page 1. Deep Learning for Natural Language Processing Solve your natural language processing problems with smart deep neural networks Karthiek Reddy Bokka, Shubhangi Hora, www.packt.com Tanuj Jain and Monicah Wanbugu Page 2 …

Emoji as a Proxy of Emotional Communication
G Santamaría-Bonfil, OGT López – Future of Robotics-Becoming …, 2019 – intechopen.com
Nowadays, emoji plays a fundamental role in human computer-mediated communications, allowing the latter to convey body language, objects, symbols, or ideas in text messages using Unicode standardized pictographs and logographs. Emoji allows people expressing more “ …

The Entity Recognition of Thai Poem Compose by Sunthorn Phu by Using the Bidirectional Long Short Term Memory Technique
O Khongtum, N Promrit, S Waijanya – International Conference on Multi …, 2019 – Springer
… of the main tasks in developing Natural language processing applied in other tasks, such as Machine translation, Information retrieval, Chat Bot and etc … In Chinese, [11] introduced the comparison between neural network and CRF model to test word vectors by n-skip-gram as an …

SafeChat System with Natural Language Processing and Deep Neural Networks
M Seedall, K MacFarlane, V Holmes – 2019 – sure.sunderland.ac.uk
… of NLP are analysis of (free) text, knowledge and abstract concept extraction from textual data (eg text understanding), generative models (eg chat bots, virtual assistants … Word2Vec is group of efficient predictive models (input, projection and output layers) • Skip-Gram model and …

High-performance intent classification in sparse supervised data conditions
K Galli – 2019 – dspace.mit.edu
… users. With the massive expansion of web-based chat, the market for chatbots is very … 1.1.1 Text Classification In the following sections, text classification work related to chatbot intent recognition … on its context. The other architecture, skip-gram, is trained to predict surrounding …

Semantic representations for under-resourced languages
J Mazarura, A de Waal, P de Villiers – … of the South African Institute of …, 2019 – dl.acm.org
… documents can be derived from this pre- sentation which leads to other practical NLP applications such as collaborative filtering, aspect-based sentiment analy- sis, intent classification for chatbots and machine … Skip-gram predicts the source context words from the target word …

Text-to-image Synthesis for Fashion Design
Z Yi – 2019 – diva-portal.org
… For instance, AI chatbots are already in use by lots of fashion retailers to connect the customers, offer per- sonalized recommendations, reduce … It is alterable by choosing one of the following components: the Continuous Bag-of-Words model (CBOW) and the Skip- Gram model …

Intent classification through conversational interfaces: Classification within a small domain
S Lekic, K Liu – 2019 – diva-portal.org
… The company is interested in text classification and in this project since their long term agenda is to develop a chat bot utilising Machine … [21].Moreover, there are two models that Word2Vec can use, continuous bag-of- words (CBOW) or skip-gram (Figure 2.3) …

Multi-Turn Response Selection in Retrieval-Based Chatbots with Iterated Attentive Convolution Matching Network
H Wang, Z Wu, J Chen – Proceedings of the 28th ACM International …, 2019 – dl.acm.org
… KEYWORDS multi-grained representation; matching; retrieval-based chatbot; multi-turn response selection; deep neural network ACM Reference Format: Heyuan Wang, Ziyi Wu, and Junyu Chen. 2019. Multi-Turn Response Se- lection in Retrieval-Based Chatbots with Iterated …

Detecting offensive language using transfer learning
A de Bruijn, V Muhonen, T Albinonistraat, W Fokkink… – 2019 – science.vu.nl
… A better understanding of language models could help to build a chatbot, or a topic model, or help with other Natural Language Processing related … It uses two different methods to learn word embeddings: Continuous Bag-of-Words (CBOW) and Continuous Skip- Gram Model …

Guiding Variational Response Generator to Exploit Persona
B Wu, M Li, Z Wang, Y Chen, D Wong, Q Feng… – arXiv preprint arXiv …, 2019 – arxiv.org
… agents (aka, chat-bots), Persona Modeling is of great importance for such deep neural network based intelligent interactive sys- tems (Li et al., 2016b; Kottur et al., 2017; Wang et al., 2017). Apparently, user-personality- dependent responses provided by a chat-bot is able to …

Towards a Self-aware Intelligent Agent
M Morisio, O Isabeau – 2019 – webthesis.biblio.polito.it
… field: therefore, these metrics can be used for the evaluation of internal content generated by intelligent conversa- tional agents, better known as chatbots … 21 2.4 CBOW and Skip-gram … Eliza and Parry are funny chat-bot that emulates a ”doc- tor” and ”schizophrenic patient” …

Knowledge-based approach to Winograd Schema Challenge
IM Boguslavsky, F TI, I LL, L AV, R IP, T SP – current volume, 2019 – dialog-21.ru
… The weakness of the Turing test be- came especially obvious after it was successfully passed in 2014 by the chatbot Eugene Goostman who assumed a false identity of a 13-year old boy from Odessa … It makes use of the skip-gram model to learn word representations …

LSTM vs Random Forest for Binary Classification of Insurance Related Text
H Kindbom – 2019 – diva-portal.org
… It is interesting to examine whether the investments and increased attention for chatbots can be justified from the … rate, or more precisely how Hedvig’s adoption rate would be affected if a chatbot … Skip-gram model, illustrated in figure 2.1 [2]. In CBOW, the model learns to …

Reconstructing capsule networks for zero-shot intent classification
H Liu, X Zhang, L Fan, X Fu, Q Li, XM Wu… – Proceedings of the 2019 …, 2019 – aclweb.org
… With the advent of conversational AI, task- oriented spoken dialogue systems are becoming ubiquitous, eg, chatbots deployed on differen- t applications, or modules … wt ? Rdw is the word embedding of the t-th word and can be pretrained by the skip-gram mod- el (Mikolov et al …

Listening between the lines: Learning personal attributes from conversations
A Tigunova, A Yates, P Mirza, G Weikum – The World Wide Web …, 2019 – dl.acm.org
… will then be a distant source of background knowledge for personalization in downstream applications such as Web-based chatbots and agents in … General-purpose chatbot-like agents show decent performance in benchmarks (eg, [13, 20, 37]), but critically rely on sufficient train …

Fast and accurate entity linking via graph embedding
A Parravicini, R Patra, DB Bartolini… – Proceedings of the 2nd …, 2019 – dl.acm.org
… identify entities contained in a text is critical in many fields of application, such as text analysis, recommender systems, semantic search and chatbots … In word2vec, using the skip-gram model, the embedding of a word wi is learnt to maximize the probability of surrounding …

From words to pixels: text and image mining methods for service research
FV Ordenes, S Zhang – Journal of Service Management, 2019 – emerald.com
… deal with text and image data, such as natural language processing, computer vision, machine learning, deep neuronal nets and chatbots are of … continuous bag of words), to predict a word according to its context (preceding and subsequent words), and Skip-gram, which aims …

Keep calm and switch on! preserving sentiment and fluency in semantic text exchange
SY Feng, AW Li, J Hoey – arXiv preprint arXiv:1909.00088, 2019 – arxiv.org
… useful for text data augmentation and the se- mantic correction of text generated by chat- bots and virtual … Another use of STE is in building emotionally aligned chatbots and virtual assistants … the original, and may be desired in cases such as creating a lively chat- bot or correcting …

Driven answer generation for product-related questions in e-commerce
S Chen, C Li, F Ji, W Zhou, H Chen – … on Web Search and Data Mining, 2019 – dl.acm.org
… 12 9 Test 1,000 11 12 9 then learnt over the combination of the rest QA pairs and reviews using Goolge’s Word2Vec toolkit4 with Skip-gram algorithm [18]. Auxiliary Review Snippet Extraction. We need to extract the auxiliary …

Deep Learning for Opinion Mining: A Systematic Survey
Y Agarwal, R Katarya… – 2019 4th International …, 2019 – ieeexplore.ieee.org
… as it uses a deep learning model called “SS-BED (Sentiment and Semantic-Based Emotion Detector).” They also develop a chatbot that can … and neural network algorithms work in opinion mining [21] as they help in word embedding’s that are used in the skip-gram model, solve …

Semantic relation classification through low-dimensional distributed representations of partial word sequences
Z Jin, C Shibata, K Tago – Nonlinear Theory and Its Applications …, 2019 – jstage.jst.go.jp
… Mikolov et al. proposed two new language models called the continuous bag-of-words (CBOW) model and the skip-gram model, both of which are a type of unsupervised NNLM [17,18] … At the beginning of experiments, we used two models, the CBOW and the Skip-gram …

GRAMMAR AND SPELL CHECKING FOR TURKISH LANGUAGE
MC Ganiz – 2019 – cse.eng.marmara.edu.tr
… Training word embeddings • Chatbots • Sentiment analysis • Text summarization 1 Page 3 … (wt+1,wt) – (“on”, “sat”) • (wt+2,wt) – (“the”, “sat”) Word2Vec has two architectures which are CBOW(Continuous bag of words) and skip-gram. These architectures are shown in Figure 11 …

A comparative study of word embedding methods for early risk prediction on the Internet
E Fano – 2019 – diva-portal.org
… It would be possible to develop chat bots and other dialogue systems that can determine the severity of a person’s mental health risk based on … comes in two different variants, according to whether it is trained using the continuous bag of words (CBOW) or the skip-gram algorithm …

Neural Approaches for Syntactic and Semantic Analysis
S Kurita – 2019 – repository.kulib.kyoto-u.ac.jp
… matical analyses of texts to natural language understanding and application systems such as machine translation systems and chatbots … as machine translation, question-answering, chatbots and searching systems are called application studies …

Efficient Algorithm for Answering Fact-based Queries Using Relational Data Enriched by Context-Based Embeddings
AA Altowayan – 2019 – webpage.pace.edu
… Page 3. Abstract Intelligent conversational systems – such as question answering and chatbots – are becoming a more critical component of today’s AI in areas ranging from health, medicine, and security, to personal assistants, and other domains …

Multiple Generative Models Ensemble for Knowledge-Driven Proactive Human-Computer Dialogue Agent
Z Dai, W Liu, G Zhan – arXiv preprint arXiv:1907.03590, 2019 – arxiv.org
… Generative method for building conversation chatbots has attracted increasing interest due to its great … For a knowledge-driven dialogue chatbot, an important ability is to reuse the given … Directional skip-gram: Explicitly distinguishing left and right context for word embeddings …

Artificial Intelligence in the legal sector. A comparative analysis of expert and AI approaches to predicting court decisions
N Kaliazina – 2019 – pdfs.semanticscholar.org
… Page 13. Figure 2. CBOW and Skip-gram Models for Text Processing The only drawback of Skip-Gram and CBOW is that they belong to the class of … Another branch of screening solutions is usually presented in the form of chatbots that to summarise it to a brief overview which …

CBET: design and evaluation of a domain-specific chatbot for mobile learning
Q Liu, J Huang, L Wu, K Zhu, S Ba – Universal Access in the Information …, 2019 – Springer
… To enhance the chatbots applied in mobile learning, we pro- pose an intelligent chatbot for the … eg, greetings, emo- tion, and humor into the embedded KB to make the chatbot more user … of word vectors word2vec.google.bin -cbow Choice of training models 0: Skip-gram model 1 …

Named Entity Resolution for Historical Texts
A Holmes – 2019 – digital.lib.washington.edu
… Think of the auto-correct on your smartphone; voice assistants such as Siri, Alexa, and Cortana; and chat bots among many other applications … [17]. 4.2.1 word2vec There are two flavors of word2vec: skip-gram and continuous bag of words (CBOW), which …

ML-based Interactive Data Visualization System for Diversity and Fairness Issues
S Min, J Kim – International Journal of Contents, 2019 – dbpia.co.kr
… This data-dependency could cause severe problems as demonstrated in the case of Microsoft’s chatbot Tay trained based on Twitter data as a racist [5]. Diversity issue may look less obvious than the above case, but lack of … We used the Skip-Gram model to train the model …

Expanding on the end-to-end memory network for goal-oriented dialogue
PA Taraldsen, V Vatne – 2019 – uia.brage.unit.no
… 2. Vatne, V., Taraldsen, PA, Jafari, R., Goodwin, M., Granmo, O- C. (2019): Dialogue Systems using End-To-End Memory Networks: Divorce Bot. In: Workshop: Chatbots for Social Good, September 3, 2019, Paphos, Cyprus (under review) …

Multi-sense embeddings through a word sense disambiguation process
T Ruas, W Grosky, A Aizawa – Expert Systems with Applications, 2019 – Elsevier
… It is undeniable that word2vec’s contributions with continuous skip-gram (SG) and continuous bag-of-words (CBOW) from Mikolov, Chen et al. (2013); Mikolov, Sutskever et al … Another modification of skip-gram is proposed by Neelakantan et al …

Towards Emotion Intelligence in Neural Dialogue Systems
C Huang – 2019 – era.library.ualberta.ca
… intelligent. In this work, the objective is to tackle two main problems that are essential towards building emotionally intelligent chatbots: “How to detect the emotions expressed by the human accurately?” and “How can a chatbot express an emotion?” …

Deep Neural Network Based Iterative Self-Taught Learning on Text Mining
X Liu – 2019 – search.proquest.com
… used for face recognition and friend recommendation. For online customer support, the chatbots understand the user queries better with pattern learned from historical online service chatting. A search engine working at the backend to improve the …

Sentence Simplification in context of Automatic Question Generation
D Yarish – 2019 – ucu.edu.ua.s3.amazonaws.com
… Besides, knowing in advance all possible questions on a particular topic al- lows the apps, services, chatbots (and not very confident speakers) prepare answers beforehand and don’t waste time on them while interacting with the user(audience) …

A Study on Named Entity Recognition for Effective Dialogue Information Prediction
M Go, H Kim, H Lim, Y Lee, M Jee… – Journal of Broadcast …, 2019 – koreascience.or.kr
… ?? ???? ??? ?? ?? ?? ?? ???(Task- Oriented Dialogue System)? ??? ?? ?? ??? (ChatBot)?? ?? ? ?? … Word2Vec ??? ???? ?? ???? ??? ??? 6?? ???? Skip-gram ??? ? 20?? ?? ??? ?? ????? …

SAC-Net: Stroke-Aware Copy Network for Chinese Neural Question Generation
W Li, Q Kang, B Xu, L Zhang – 2019 IEEE International …, 2019 – ieeexplore.ieee.org
… Question generation can also be used to generate exercises and assignments in the field of education [6-8]. In addition, it can help chat bots have cold-to-start or continue conversations with human users [9] … [29] Song, Yan, et al. “Directional Skip-Gram: Explicitly Distinguishing …

Computational linguistics: Introduction to the thematic issue
A Gelbukh – Computación y Sistemas, 2019 – cys.cic.ipn.mx
… Language” write: In this paper we evaluate our new approach based on the Continuous Bag-of-Words and Skip-gram models enriched … Techniques for short vs Variable Length Text using Word Embeddings” write: In goal-oriented conversational agents like Chatbots, finding the …

Folksonomy Based Question Answering System
S Ramaswamy – 2019 – utd-ir.tdl.org
… 12 2.1.1.2.1.1 INPUTS: …. 12 2.1.1.2.1.2 SKIP-GRAM MODEL: …. 13 … 10 2.3 Vector calculation using skip-gram …………… 14 …

End-to-End Neural Context Reconstruction in Chinese Dialogue
W Yang, R Qiao, H Qin, A Sun, L Tan… – Proceedings of the First …, 2019 – aclweb.org
… 1 Introduction The chatbot is claimed to become a platform for the next generation of the human-computer in- terface … The Chinese word embeddings are pre-trained us- ing skip-gram model (Mikolov et al., 2013) on the raw CQA corpus …

Mobile Medical Question and Answer System with Improved Char-level based Convolution Neural Network and Sparse Auto Encoder
G Yan, J Li – Proceedings of the 2019 Asia Pacific Information …, 2019 – dl.acm.org
… With the advent of Siri and the emergence of major manufacturers of different chatbots, question-answering systems (QA systems … 18] discovered the semantic relationship between word vectors and proposed Continuous Bag-of-Words Model and Skip-gram model architectures …

A Relation Proposal Network for End-to-End Information Extraction
Z Liu, T Wang, W Dai, Z Dai, G Zhang – CCF International Conference on …, 2019 – Springer
… to many Artificial Intelligence (AI) applications, such as Information Retrieval (IR), Intelligent Question and Answering (QA), and Intelligence Chat-bots (IC) … 91–99 (2015)Google Scholar. 4. Song, Y., Shi, S., Li, J., Zhang, H.: Directional skip-gram: explicitly distinguishing left and …

Towards effective and interpretable person-job fitting
R Le, W Hu, Y Song, T Zhang, D Zhao… – Proceedings of the 28th …, 2019 – dl.acm.org
… 3.2 Intention Model Unlike the matching scenarios in Question Answering [11, 28] and Retrieval Based Chatbot [17, 18], where the target is to find … We represent the words in job postings and resumes with 100-dimension pre-trained Skip-Gram vectors [13] which are fixed during …

From medical records to research papers: A literature analysis pipeline for supporting medical genomic diagnosis processes
FL Bello, H Naya, V Raggio, A Rosá – Informatics in Medicine Unlocked, 2019 – Elsevier
… Chiu et al. [23] devise guidelines for good word2vec based embeddings, both CBOW and skip-gram, working on PubMed and the PMC corpus. For auxiliary tasks, these authors use GeniaSS as a sentence splitter and NLTK [24] for word tokenizing …

Deep Learning for Session Aware Conversational Agents
M Morisio, MA Senese – 2019 – webthesis.biblio.polito.it
… monitor asthmatics children, shows how this type of technique could be an important piece in the development of future chatbots. iv … 19 Skip-Gram … In 1972 the psychiatrist Kenneth Colby implemented PARRY a chatbot that simulates a person affected by paranoid schizophrenia …

Recurrent neural models and related problems in natural language processing
S Zhang – 2019 – papyrus.bib.umontreal.ca
… Le quatri`eme article aborde le probl`eme du manque de personnalité des chatbots. Le jeu de données persona-chat que nous proposons ii Page 3 … The fourth article tackles the problem of the lack of personality in chatbots …

Personalized recommendation: neural architectures and beyond.
S Zhang – 2019 – unsworks.unsw.edu.au
… They offer a wide range of applications in object detection, person re-identification, machine translation, au- tonomous driving, speech recognition, chat-bot, etc. Neural networks can process real-world complex input data and are scalable to large-scale datasets. With enough …

Parallel computing approaches for dimensionality reduction in the high-dimensional data
SV Patil, DB Kulkarni – Third National Research Symposium on …, 2019 – rsc.wce.ac.in
… This paper reviews different sentiment analysis techniques and it especially makes a comment on Bag of Words (Bow), Skip-Gram Model, Word2Vec, GloVe algorithm for word embedding methods of Deep Learning, TF-IDF method etc …

Understanding Emoji Interpretation through User Personality and Message Context
ST Völkel, D Buschek, J Pranjic… – Proceedings of the 21st …, 2019 – dl.acm.org
Page 1. Understanding Emoji Interpretation through User Personality and Message Context Sarah Theres Völkel, Daniel Buschek, Jelena Pranjic, Heinrich Hussmann LMU Munich, Munich, Germany sarah.voelkel,daniel.buschek …

APPLYING NATURAL LANGUAGE PROCESSING IN TEXT BASED SUPPLIER DISCOVERY
J Heikkilä, J Kanniainen – 2019 – trepo.tuni.fi
… develop. Nowadays capabilities of NLP have been applied to domains like speech recognition, lexical analysis, text summarization, chatbots, text tagging etc (Hardeniya et al. 2016; Deng & Liu 2018; Goyal et al. 2018). Commonly …

Proposed Model for Arabic Grammar Error Correction Based on Convolutional Neural Network
A Solyman, Z Wang, Q Tao – 2019 International Conference on …, 2019 – ieeexplore.ieee.org
… Sequence to sequence (seq2seq) or decoder-encoder model [13], has successfully applied in applications such as online chatbots, Google Translate … word embeddings calculated by representing a word as a set of characters N-grams and merging the skip-gram embeddings …

Informatics in Medicine Unlocked
FL Bello, H Naya, V Raggio, A Rosá – researchgate.net
… Chiu et al. [23] devise guidelines for good word2vec based embeddings, both CBOW and skip-gram, working on PubMed and the PMC corpus. For auxiliary tasks, these authors use GeniaSS as a sentence splitter and NLTK [24] for word tokenizing …

Pumice: A multi-modal agent that learns concepts and conditionals from natural language and demonstrations
TJJ Li, M Radensky, J Jia, K Singarajah… – Proceedings of the …, 2019 – dl.acm.org
… For the next phase, PUMICE has already determined that “order a cup of Iced Cappuccino” should be an action triggered when the condition “it’s hot” is true, but does not know how to perform this action (also known as intent fulfillment in chatbots [23]) …

Hierarchy Response Learning for Neural Conversation Generation
B Zhang, X Zhang – Proceedings of the 2019 Conference on Empirical …, 2019 – aclweb.org
… As for HAE, we initialize the embedding of dialog acts using three different methods: (1) RD (random): initializing the embedding ran- domly; (2) LG (logic-related): training a Skip- Gram model (Mikolov et al., 2013) to maximize the co-occurrence probability among the acts that …

Classification-based approach for Question Answering Systems: Design and Application in HR operations
LMA Heijden – 2019 – essay.utwente.nl
… These can be either so-called dialogue managers, where the chatbot responds based on a humanly designed way to certain intents defined by humans during the design [16, 17, 18]. Other chatbots generate answers using machine learning techniques such as …

The Impact of Toxic Replies on Twitter Conversations
N Salehabadi – 2019 – rc.library.uta.edu
… Embedding). Decoder decodes the vectors to a sentence (Song et al., 2018). Natural Language Processing (NLP) Techniques are used in Question-Answering, Chatbots and dialog systems. Generative models are state of the art for generating text, and …

Metro maps for efficient knowledge learning by summarizing massive electronic textbooks
W Lu, P Ma, J Yu, Y Zhou, B Wei – International Journal on Document …, 2019 – Springer
… not suitable. On the other hand, unsupervised approaches like sentence embeddings [16], which was inspired by the Skip-gram model, can derive sentence vectors for sentence similarity calculation. However, using sentence …

Contributions to Clinical Information Extraction in Portuguese: Corpora, Named Entity Recognition, Word Embeddings
FAC Lopes – 2019 – estudogeral.sib.uc.pt
… Image extracted from https://cocoxu.github.io/ courses/5525_slides_spring17/17_crf.pdf. . . . . 12 2.5 Skip-gram and Continuous Bag-of-words architectures for the Word2Vec algorithm using the following sentence “…brain is the central organ of the…” …

Explainable and Transferrable Text Categorization
T Eljasik-Swoboda, F Engel, M Hemmje – International Conference on …, 2019 – Springer
… Among these problems are intent detection for chatbots or hate speech detection for social media … Notable implementations are Word2Vec’s CBOW, skip-gram, and GloVe [18, 19] … A primary task for digital assistants or chat bots is intent recognition: The detection of the task that …

A discrete cvae for response generation on short-text conversation
J Gao, W Bi, X Liu, J Li, G Zhou, S Shi – arXiv preprint arXiv:1911.09845, 2019 – arxiv.org
… the generator and discrimina- tor jointly. Deep reinforcement learning is also applied to model future reward in chatbot after an encoder-decoder model converges (Li et al., 2016c, 2017). The above methods directly inte- grate …

Sentiment Analysis through Transfer Learning for Turkish Language
SE Akin, T Yildiz – … on INnovations in Intelligent SysTems and …, 2019 – ieeexplore.ieee.org
… to boost the performance of reinforcement learning- based goal-oriented chatbots for restaurant … Sentiment analysis of spanish tweets using a ranking algorithm and skipgrams … [50] V. Ilievski, C. Musat, A. Hossman, and M. Baeriswyl, “Goal-Oriented Chatbot Dialog Management …

Knowledge-based Conversational Search
S Vakulenko – arXiv preprint arXiv:1912.06859, 2019 – arxiv.org
… Sophia Keyner, Vadim Savenkov, and Svitlana Vakulenko. Open data chatbot. In The Semantic Web: ESWC 2019 Satellite Events, Portorož, Slovenia, 2019 • Svitlana Vakulenko, Ilya Markov, and Maarten de Rijke. Conversational exploratory search via interactive storytelling …

Detection of Difficult for Understanding Medical Words using Deep Learning
H Pylieva – 2019 – er.ucu.edu.ua
Page 1 …

Extracting and Learning Semantics from Social Web Data
T Niebler – 2019 – opus.bibliothek.uni-wuerzburg.de
Page 1. Thomas Niebler Extracting and Learning Semantics from Social Web Data Dissertation zur Erlangung des akademischen Grades eines Dok- tors der Naturwissenschaften (Dr. rer. nat.) in der Fakultät für Mathematik …

Deep learning for nlp and speech recognition
U Kamath, J Liu, J Whitaker – 2019 – Springer
Page 1. Uday Kamath · John Liu · James Whitaker Deep Learning for NLP and Speech Recognition Page 2. Deep Learning for NLP and Speech Recognition Page 3. Uday Kamath • John Liu • James Whitaker Deep Learning for NLP and Speech Recognition 123 Page 4 …

Deep learning for database mapping and asking clarification questions in dialogue systems
M Korpusik, J Glass – IEEE/ACM Transactions on Audio …, 2019 – ieeexplore.ieee.org
… showed that deep RL models enabled chatbots to generate more diverse, informative, and … approach with CNN-learned embeddings significantly outperforms re- ranking with skipgram embeddings [42 … achieves 64.8% top-5 recall, whereas re-ranking with skipgrams only yields …

Security vulnerability information service with natural language query support
C Rodriguez, S Zamanirad, R Nouri, K Darabal… – International Conference …, 2019 – Springer
… We trained the model using Word2Vec [12] and a skip-gram model with negative sampling (sampling rate of 10 words), 300 … it a good candidate for integration into productivity tools used in software development and devops environments (eg, through chatbots), which can help …

Utterance Intent Classification for Spoken Dialogue System with Data-Driven Untying of Recursive Autoencoders
T Kato, A Nagai, N Noda, J Wu… – … on Information and …, 2019 – search.ieice.org
Page 1. IEICE TRANS. INF. & SYST., VOL.E102–D, NO.6 JUNE 2019 1197 PAPER Utterance Intent Classification for Spoken Dialogue System with Data-Driven Untying of Recursive Autoencoders Tsuneo KATO †a) , Member …

Automatic Detection of Emotions and Distress in Textual Data
E Mohammadi – 2019 – caiac.ca
Page 1. Automatic Detection of Emotions and Distress in Textual Data Elham Mohammadi A Thesis in The Department of Computer Science and Software Engineering Presented in Partial Fulfillment of the Requirements for the Degree of Master of Computer Science at …

Machine learning methods for adaptive test case generation for Android activities
A Cardone – 2019 – indigo.uic.edu
… 20 5 An example of LEB128 encoding with just 2 bytes. . . . . 21 6 A section of the binary vector for Requested Permissions (60 cells). . . 26 7 Graphical representation of CBOW and Skip-gram Word2Vec architec- tures. . . . . 30 …

Interview choice reveals your preference on the market: To improve job-resume matching through profiling memories
R Yan, R Le, Y Song, T Zhang, X Zhang… – Proceedings of the 25th …, 2019 – dl.acm.org
Page 1. Interview Choice Reveals Your Preference on the Market: To Improve Job-Resume Matching through Profiling Memories Rui Yan ? Institute of Computer Science and Technology, Peking University Beijing, China ruiyan@pku.edu.cn Ran Le ? …

Utterance-to-utterance interactive matching network for multi-turn response selection in retrieval-based chatbots
JC Gu, ZH Ling, Q Liu – IEEE/ACM Transactions on Audio …, 2019 – ieeexplore.ieee.org
… I. INTRODUCTION BUILDING a chatbot that can converse naturally with hu- mans on open … given the context of a conversation, is the key technique for building retrieval-based chatbots … matching network (U2U- IMN) for multi-turn response selection in retrieval-based chat- bots …

Exploring automatic approaches for sentiment lexicon creation for Norwegian
KK Amundsen – 2019 – duo.uio.no
… experiments. . . . . 71 5.5 Evaluation results: lexicons from fine-tuned Skipgram models. . . . … 2014). Another application of sentiment analysis is in the field of chatbots and other systems used for communication (Turney & Littman, 2003) …

Predicting User’s Intent from Text using Machine Learning Methods
A Katsalis – 2019 – repository.ihu.edu.gr
… It is a challenge to create a robust model which can deal with problems from other domains. Search engines, spoken language understanding (SLU) systems, chatbots and even … tries to estimate a word using as features the surrounding words. The Skip-gram model …

A review of the analytics techniques for an efficient management of online forums: An architecture proposal
J Peral, A Ferrandez, H Mora, D Gil… – IEEE Access, 2019 – ieeexplore.ieee.org
Page 1. 2169-3536 (c) 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org …

Deep learning for spoken dialogue systems: application to nutrition
MB Korpusik – 2019 – dspace.mit.edu
… 49 2-7 The word2vec skip-gram architecture, where the objective is to learn word vectors that are good at predicting nearby words (Mikolov et al., 2013b). . 50 2-8 A plot of country and capital city words’ vectors learned by word2vec, and …

Exploring the opportunities of the emojis in brand communication: The case of the beer industry
AM Casado-Molina… – … Journal of Business …, 2019 – journals.sagepub.com
This study shows that emojis are a significant element in brand communications, which still requires attention from researchers. Specifically, it describes the use of emojis by the four companies w…

Predicting the Emotional Intensity of Tweets
IM Alhamdan – 2019 – scholarworks.rit.edu
… and Torre, 2015). Chatbots are widely used for customer service, and being more aware of a customers attitude could help improve Page 40. 2.2. Affective Computing 15 chatbot’s interaction with a customer (Portela and Granell-Canut …

Structured Knowledge Discovery from Massive Text Corpus
C Zhang – arXiv preprint arXiv:1908.01837, 2019 – arxiv.org
… As voice assistants and chat-bots become more and more popular, users may ask smart devices questions via voice … For example, booking a flight with customer service representatives. Figure 1 illustrates three scenarios on community Q&A, voice assistant/chatbot, and service …

Explaining black-box models in the context of Natural Language Processing
T Cerquitelli, S Greco – webthesis.biblio.polito.it
… discarding cv on job applications, or by analyzing the sentiment for marketing purpose, or by un- derstand query on the search engine or again by creating chatbot that make … Skip-Gram: is slower compared with the other method but gives a better representation of rare words …

Unsupervised Text Representation Learning with Interactive Language
H Cheng – 2019 – digital.lib.washington.edu
… leverage the embeddings of surrounding sentences to improve the sentence embeddings, analogous to learning the word embeddings from context words in the skip-gram model … (aka chatbots) have been developed for entertainment, companionship and education purpose …

Semantic and Discursive Representation for Natural Language Understanding
D Sileo – 2019 – tel.archives-ouvertes.fr
… Under- standing the needs of humans paves the way for their automatic fulfilment (as in chatbot systems, robotics or information retrieval) … erating costs. Some tasks can rely on other tasks; for instance, a chatbot system (1-1c) can be decomposed into modules …

Neural Networks with Keras Cookbook: Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots
VK Ayyadevara – 2019 – books.google.com
… Measuring the similarity between word vectors Building a word vector using the skip-gram and CBOW models Getting ready How to … Chapter 13: Sequence-to-Sequence Learning Introduction Returning sequences of outputs from a network Building a chatbot Getting ready How …

Semantic Feature Extraction Using Multi-Sense Embeddings and Lexical Chains
TL Ruas – 2019 – deepblue.lib.umich.edu
… CRF Conditional Random Fields CNN-MSSG Convolutional Neural Network Multi-Sense Skip-Gram CWS Context Word Similarity … MSSA-NR Most Suitable Sense Annotation – N Refined MSSG Multi-Sense Skip-Gram MT-DNN Multi-Task Deep Neural Network NB Naïve Bayes …

Ontological Traceability using Natural Language Processing
E Rosa Benitez – 2019 – dspace.library.uu.nl
Page 1. Ontological Traceability using Natural Language Processing A master thesis presented by Edder de la Rosa Benitez Submitted to the Department of Organization and Information in partial fulfillment of the requirements for the degree of Master of Science in …

Chatting about data
L Martinico – project-archive.inf.ed.ac.uk
… By hosting the chat- bot on the Facebook Messenger platform, Healthbot was given a privileged placement, as one of the contacts in a user’s address book … In this private conversation mode, use of chatbots is disabled; the only commercial chatbot platforms that provide …

Contextual language understanding Thoughts on Machine Learning in Natural Language Processing
B Favre – 2019 – hal-amu.archives-ouvertes.fr
… General purpose dialog agents, also known as “chatbots”, are a good example of how humans can be deceived in thinking that they are … The ELIZA chatbot (Weizenbaum 1976) or contestants to the Loeb- ner Prize competition (Stephens 2004) are dialog systems which rely on …

Neural Approaches to Sequence Labeling for Information Extraction
I Bekoulis – 2019 – biblio.ugent.be
Page 1. Page 2. Page 3. Neural Approaches to Sequence Labeling for Information Extraction Neurale netwerkoplossingen voor het labelen van tekstsequenties bij informatie-extractie Ioannis Bekoulis Promotoren: prof. dr. ir. C. Develder, dr. ir …

Narrative Text Generation via Latent Embedding from Visual Stories
??? – 2019 – s-space.snu.ac.kr
Page 1. ?????-???-???? 2.0 ???? ???? ??? ??? ??? ??? ??? ???? ? ? ???? ??, ??, ??, ??, ?? ? ??? ? ????. ??? ?? ??? ??? ???: ? ???, ? ???? …

On language and structure in polarized communities
M Lai – 2019 – riunet.upv.es
Page 1. Universitat Politècnica de València Departamento de Sistemas Informáticos y Computación Tesis de Doctorado en Informática Mirko Lai Language and Structure in Polarized Communities Directores de Tesis Giancarlo …

Enriching a question-answering system with user experience concepts
LV Simons – 2019 – dspace.library.uu.nl
… consideration to produce a single answer as a response. This is different from question-answering through a chatbot, in which a dialog is produced. Dialogs are not in scope. ? The use of the word ‘handling’ is intentionally because …

A Review of the Analytics Techniques for an Efficient Management of Online Forums: An Architecture Proposal
J Peral Cortés, A Ferrández, H Mora, D Gil… – 2019 – rua.ua.es
Page 1. SPECIAL SECTION ON APPLICATIONS OF BIG DATA IN SOCIAL SCIENCES Received December 5, 2018, accepted January 9, 2019, date of publication January 15, 2019, date of current version February 6, 2019 …

A Multi-Modal Intelligent Agent that Learns from Demonstrations and Natural Language Instructions
TJJ Li – 2019 – pdfs.semanticscholar.org
Page 1. A Multi-Modal Intelligent Agent that Learns from Demonstrations and Natural Language Instructions Ph.D. Thesis Proposal Toby Jia-Jun Li Human-Computer Interaction Institute Carnegie Mellon University tobyli@cs.cmu.edu http://toby.li/ November 26, 2019 …

Response Retrieval in Information-seeking Conversations
L Yang – 2019 – scholarworks.umass.edu
Page 1. University of Massachusetts Amherst ScholarWorks@UMass Amherst Doctoral Dissertations Dissertations and Theses 2019 Response Retrieval in Information-seeking Conversations Liu Yang College of Information and Computer Sciences, UMass Amherst …

Commonsense reasoning for natural language understanding: A survey of benchmarks, resources, and approaches
S Storks, Q Gao, JY Chai – arXiv preprint arXiv:1904.01172, 2019 – researchgate.net
Page 1. COMMONSENSE REASONING FOR NATURAL LANGUAGE UNDERSTANDING: ASURVEY Commonsense Reasoning for Natural Language Understanding: A Survey of Benchmarks, Resources, and Approaches Shane Storks STORKSSH@MSU.EDU Qiaozi Gao …

Learn TensorFlow 2.0
IM Learning, P Singh, A Manure – Springer
Page 1. Learn TensorFlow 2.0 Implement Machine Learning and Deep Learning Models with Python — Pramod Singh Avinash Manure Page 2. Learn TensorFlow 2.0 Implement Machine Learning and Deep Learning Models with Python Pramod Singh Avinash Manure Page 3 …

Recent advances in natural language inference: A survey of benchmarks, resources, and approaches
S Storks, Q Gao, JY Chai – arXiv preprint arXiv:1904.01172, 2019 – arxiv.org
Page 1. RECENT ADVANCES IN NATURAL LANGUAGE INFERENCE:ASURVEY Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches Shane Storks SSTORKS@UMICH.EDU …

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