Relation Extraction & Chatbots

Notes:

Relation extraction is a process in natural language processing (NLP) that involves identifying and extracting relationships between entities in a text. These relationships can be binary (e.g., “X is the parent of Y”) or n-ary (e.g., “X, Y, and Z are all members of the same organization”).

Relation extraction is a sub-task of information extraction, which aims to extract structured information from unstructured text. The extracted relations can be represented in various forms such as RDF triples, or in tabular format.

There are different techniques used for relation extraction, including rule-based methods, machine learning-based methods, and deep learning-based methods.

Rule-based methods involve manually defining a set of rules to extract relations based on patterns in the text. Machine learning-based methods involve training a model on a labeled dataset to learn to extract relations from text. Deep learning-based methods, on the other hand, use neural networks to extract relations from text.

Relation extraction can be used for various applications such as knowledge base population, question answering, and information retrieval. Relation extraction can be used in combination with dialog systems, such as chatbots, to improve their ability to understand and respond to user inputs.

One way relation extraction can be used in chatbots is by identifying entities and relationships within user inputs, and using this information to generate more accurate and contextually relevant responses. For example, if a user says “I am looking for a hotel in New York City,” a chatbot could use relation extraction to identify the entities “hotel” and “New York City” and the relationship “located in,” and use this information to generate a response such as “I’ve found several hotels in New York City. Which one would you like to know more about?”

Another way relation extraction can be used in chatbots is by extracting and utilizing the relationships between entities in a knowledge base to answer user questions. For example, if a user asks “What is the capital of France?” the chatbot could use relation extraction to identify the relationship “capital of” between the entities “Paris” and “France” in its knowledge base, and use this information to generate a response such as “The capital of France is Paris.”

  • Semantic engineering is the process of designing and developing systems that can understand and reason about the meaning of information. It is a field of artificial intelligence that focuses on creating systems that can interpret and understand the meaning of natural language text, speech, or other forms of data.
  • Semantics engineering is the process of creating and managing the meaning of information in a systematic way. It involves the study of how meaning is represented and communicated in natural language, and the design of methods and technologies for creating, editing, and maintaining the meaning of information.

Resources:

Wikipedia:

References:

See also:

100 Best Relation Extraction Videos | Automatic Relation Extraction

Social Relation Extraction from Chatbot Conversations: A Shortest Dependency Path Approach
M Glas – SKILL 2019-Studierendenkonferenz Informatik, 2019 – dl.gi.de
Digital dialog systems, also known as chatbots, often lack in the sense of a human-like and individualized interaction. The ability to learn someoneŠs social relations during conversations can lead to more personal responses and therefore to a more human-like and …

Transforming the communication between citizens and government through AI-guided chatbots
A Androutsopoulou, N Karacapilidis, E Loukis… – Government Information …, 2019 – Elsevier
… Advanced text mining techniques such as relation extraction, similarity learning, and argumentation mining will help to extract valuable … the abovementioned services add intelligence to the functionality and user interfaces of existing chatbots (and chatbot builders), the …

CONVERSATIONAL CHATBOT SYSTEM FOR STUDENT SUPPORT IN ADMINISTRATIVE EXAM INFORMATION
HA Rasheed, J Zenkert, C Weber, M Fathi – researchgate.net
… A simulation interfaces can be also added to the system for testing. In order to analyze the input sentence from the user, chatbots utilize methods of Natural Language Processing (NLP) … Chatbot Text Manager … Relation Extraction Response Phrasing Scenario Planer …

When to Talk: Chatbot Controls the Timing of Talking during Multi-turn Open-domain Dialogue Generation
T Lan, X Mao, H Huang, W Wei – arXiv preprint arXiv:1912.09879, 2019 – arxiv.org
… Table 1: In this case, chatbot and human play the role B. The existing chatbots … et al., 2017): (1) Open-domain dialogue sys- tems, also known as chatbots, have daily … of the semantic units and achieve better performance such as text classification and relation extraction (Peng et …

Rel4KC: A Reinforcement Learning Agent for Knowledge Graph Completion and Validation
X Lin, P Subasic, H Yin – Workshop on Deep Reinforcement Learning …, 2019 – cse.msu.edu
… language processing (NLP) tasks such as information retrieval, question-answering, chat-bot, machine reading … tested using a real- world knowledge graph designed for chatbot development. This RL agent identifies incorrect “facts” from relation extraction resulting from noise in …

Transforming a Specialized Q&A System to a Chatbot System: A Case of a Simplified Taxation in Korea
J Jang, K Lee – International Conference on Human-Computer …, 2019 – Springer
… use the traditional category system of the special knowledge as well as an automated semantic relation extraction from the … Type C is a type that finds general knowledge through Q&A chatbot. Nowadays, it mainly focuses on chatbots that provide information on a specific topic …

Learning from dialogue after deployment: Feed yourself, chatbot!
B Hancock, A Bordes, PE Mazare, J Weston – arXiv preprint arXiv …, 2019 – arxiv.org
… bot participates in are sliced into two complemen- tary datasets—one largely protected from the chat- bot’s mistakes (DIALOGUE … Other applications of weak supervision to dialogue (Mallinar et al., 2019) and relation extraction have observed simi- lar … 3 The Self-Feeding Chatbot …

Intelligent Chatbot for Requirements Elicitation and Classification
CSRK Surana, DB Gupta… – 2019 4th International …, 2019 – ieeexplore.ieee.org
… Entity extraction which is based on semantic technology includes entity relation extraction, linking and fact extraction [10] … [9] Rahman, AM, Al Mamun, A. and Islam, A., 2017, December. Programming challenges of chatbot: Current and future prospective … Build Better Chatbots …

Assuring Chatbot Relevance at Syntactic Level
B Galitsky – Developing Enterprise Chatbots, 2019 – Springer
… Developing Enterprise Chatbots pp 121-162 | Cite as. Assuring Chatbot Relevance at Syntactic Level. Authors; Authors and affiliations. Boris Galitsky. Chapter First Online: 05 April 2019. 935 Downloads. Abstract. In this chapter …

A Text-Generated Method to Joint Extraction of Entities and Relations
S Xiao, M Song – Applied Sciences, 2019 – mdpi.com
… Next Article in Special Issue Multi-Turn Chatbot Based on Query-Context Attentions and Dual Wasserstein Generative Adversarial Networks … Abstract. : Entity-relation extraction is a basic task in natural language processing, and recently, the use of deep-learning methods …

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 …

Learning the extraction order of multiple relational facts in a sentence with reinforcement learning
X Zeng, S He, D Zeng, K Liu, S Liu, J Zhao – Proceedings of the 2019 …, 2019 – aclweb.org
… Zeng et al. (2018b) proposed an sequence-to-sequence model with copy mechanism to handle the over- lapping problem in multiple relation extraction … Li et al. (2016) applied policy gra- dient method to model future reward in chatbot dialogue …

ArgueBot: Enabling debates through a hybrid retrieval-generation-based chatbot
I Kulatska – 2019 – essay.utwente.nl
… 2.2 Chatbots 9 Page 15. like the chatbot is. Chatbots can also be evaluated by conducting user tests and using surveys to determine user satisfaction (Higashinaka et al., 2018). 2.3 Conclusion Concluding the literature review …

Assessing the factual accuracy of generated text
B Goodrich, V Rao, PJ Liu, M Saleh – Proceedings of the 25th ACM …, 2019 – dl.acm.org
… This was modeled as a graph- ical model over latent variables. Riedel et al. [26] treated relation extraction as reasoning with matrix-factorization, and could work with surface-form texts and knowledge-base embeddings simul- taneously …

Construction of a Knowledge Graph for Query System
AT Bedadur, DS Vaishali – pdfs.semanticscholar.org
… Chatbots built on FAQs are not able answer this kind of questions.to design a human friendly question answering interface with higher level of accuracy … [5] Y P. Surmenok, “Chatbot Architecture – Pavel … Distantly supervised web relation extraction for knowledge base population …

Natural Language Processing and Conversational Shopping
L Luce – Artificial Intelligence for Fashion, 2019 – Springer
… An example of how a machine might use relation extraction can be seen in the following text and … Chatterbots—Another name for a chatbot, chatterbot is no longer popular terminology. Some might suggest that chatterbots refer to a particular class of chatbots created before 1980 …

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
… Several of these processing concepts are present in our work. 2.4. Relation extraction … Precision: 87%. 4.3. Relation extraction. For relation extraction, we perform a manual evaluation of three relatively rare disorders: glaucoma, Wilms tumor, and pancreatitis …

A Knowledge Graph Based Approach for Automatic Speech and Essay Summarization
K Khadilkar, S Kulkarni… – 2019 IEEE 5th …, 2019 – ieeexplore.ieee.org
… When we carry out Relation Extraction (Step 3), we get 2 relations: TABLE V. RELATIONS EXTRACTED – EXAMPLE 2 Relations Extracted … Using Knowledge Graphs, it will be possible to develop user specific chatbots based on the user’s language preferences …

Informatics in Medicine Unlocked
FL Bello, H Naya, V Raggio, A Rosá – researchgate.net
… We constructed a pipeline that gathers several genetics- and genomics-related resources and applies natural language processing techniques, which include named entity recognition and relation extraction … 2.4. Relation extraction …

Thai Scene Graph Generation from Images and Applications
P Khuphiran, S Kajkamhaeng… – 2019 23rd International … – ieeexplore.ieee.org
… The second approach is to use object detection and use attribute extraction and relation extraction to generate … In this application, the test sentence for the chat bot does not need to be grammatically correct … 5. Messenger Chatbot user response with scene graph and sentences …

Question Answering on Structured Data using NLIDB Approach
V Wudaru, N Koditala, A Reddy… – 2019 5th International …, 2019 – ieeexplore.ieee.org
… query approach. This approach is demonstrated using Movie domain chatbot and can also be extended to different domains. The … word. C. Dependency Parsing Further Dependency parsing is used for Relation Extraction. The tokenized …

The Cognitive Workbench: Using Artificial Intelligence in the Content Analysis of Change Experiences
R Assadollahi – Radical Change in Everyday Life, 2019 – Springer
… Since frequencies are also counted, this is a relatively cheap but coarse way (relation extraction would make it … of such a system has to be tested, as dialogs with chat bots are currently … However, even the first chat bot “Eliza,” created by Joseph Weizenbaum in the 1960s, led to …

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
Page 1. NOLTA, IEICE Paper Semantic relation classification through low-dimensional distributed representations of partial word sequences Zhan Jin 1a), Chihiro Shibata 1b), and Kazuya Tago 1c) 1 School of Computer Science …

Wordnet as a Relational Semantic Dictionary Built on Corpus Data
M Piasecki, A Dziob – CLARIN, 2019 – videolectures.net
Page 1. CLARIN-PL Wordnet as a Relational Semantic Dictionary Built on Corpus Data Maciej Piasecki, Agnieszka Dziob Wroc?aw University of Science and Technology G4.19 Research Group maciej.piasecki@pwr.edu.pl 2019-09-03 Page 2. Plan …

Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
M Bansal, A Villavicencio – Proceedings of the 23rd Conference on …, 2019 – aclweb.org
… Deep Structured Neural Network for Event Temporal Relation Extraction Rujun Han, I-Hung Hsu, Mu Yang, Aram Galstyan, Ralph Weischedel … Incorporating Interlocutor-Aware Context into Response Generation on Multi-Party Chatbots Cao Liu, Kang Liu, Shizhu He, Zaiqing …

Improving question generation with to the point context
J Li, Y Gao, L Bing, I King, MR Lyu – arXiv preprint arXiv:1910.06036, 2019 – arxiv.org
… image (Mostafazadeh et al., 2016). QG is an in- creasingly important area in NLP with various ap- plication scenarios such as intelligence tutor sys- tems, open-domain chatbots and question answer- ing dataset construction … 2.2 Answer-relevant Relation Extraction …

Proceedings of the 2019 Workshop on Widening NLP
A Axelrod, D Yang, R Cunha, S Shaikh… – Proceedings of the 2019 …, 2019 – aclweb.org
… A Framework for Relation Extraction Across Multiple Datasets in Multiple Domains Geeticka Chauhan … Implementing a Multi-lingual Chatbot for Positive Reinforcement in Young Learners … Exploring Social Bias in Chatbots using Stereotype Knowledge Nayeon Lee, Andrea …

Neural Approaches to Sequence Labeling for Information Extraction
I Bekoulis – 2019 – biblio.ugent.be
… 87 Page 11. v 4A.2.3 Joint entity and relation extraction . . . . . 87 4A.3 Joint model … 91 4A.3.3 Named entity recognition . . . . . 91 4A.3.4 Relation extraction as multi-head selection . . . . . 93 4A.3.5 Edmonds’algorithm …

Contributions to Clinical Information Extraction in Portuguese: Corpora, Named Entity Recognition, Word Embeddings
FAC Lopes – 2019 – estudogeral.sib.uc.pt
… 1 POS Part-of-Speech. 8, 23, 26, 32, 34, 42, 46, 52 RE Relation Extraction. 2, 8 RNN Recurrent Neural Network … performing Information Extraction (IE): the Named Entity Recognition (NER) and Relation Extraction (RE). While NER is responsible for recognising concepts that …

A Simple Guide to Implement Data Retrieval through Natural Language Database Query Interface (NLDQ)
T Ahmad, N Ahmad – 2019 8th International Conference System …, 2019 – ieeexplore.ieee.org
… This includes searching[9], classification, clustering and chatbots – virtual assistants like Siri, Alexa, Google Assistant, Cortana, Watson[10], [11]a named entity recognizer, a co-reference resolution component, and a relation extraction component …

Web and Big Data
J Song, X Zhu – 2019 – Springer
… 172 Xuanxing Yang, Guozheng Rao, and Zhiyong Feng DDC 2017 Finding Optimal Team for Multi-skill Task in Spatial Crowdsourcing….. 185 Qian Tao, Bowen Du, Tianshu Song, and Ke Xu CI-Bot: A Hybrid Chatbot Enhanced by Crowdsourcing …

DyKgChat: Benchmarking dialogue generation grounding on dynamic knowledge graphs
YL Tuan, YN Chen, H Lee – arXiv preprint arXiv:1910.00610, 2019 – arxiv.org
Page 1. DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs Yi-Lin Tuan Yun-Nung Chen Hung-yi Lee National Taiwan University, Taipei, Taiwan pascaltuan@gmail.com yvchen@ieee.org hungyilee@ntu.edu.tw Abstract …

Conversational AI: An Overview of Methodologies, Applications & Future Scope
P Kulkarni, A Mahabaleshwarkar… – 2019 5th …, 2019 – ieeexplore.ieee.org
… [14] Zheng, Suncong, et al. “Joint Entity and Relation Extraction Based on a … Goaloriented chatbot dialog management bootstrapping with transferlearning.arXiv preprint arXiv:1802.00500 … A Survey on Conversational Agents/Chatbots Classification and Design Techniques …

Linguistic classification: dealing jointly with irrelevance and inconsistency
L Franzoi, A Sgarro, A Dinu, LP Dinu – 12th International Conference on …, 2019 – arts.units.it
… 373 On a Chatbot Providing Virtual Dialogues Boris Galitsky, Dmitry Ilvovsky and Elizaveta Goncharova … 639 The Impact of Semantic Linguistic Features in Relation Extraction: A Logical Relational Learning Ap- proach Rinaldo Lima, Bernard Espinasse and Frederico Freitas …

Lawyer’s Intellectual Tool for Analysis of Legal Documents in Russian
A Khasianov, I Alimova, A Marchenko… – … and Innovations (IC …, 2019 – ieeexplore.ieee.org
… architecture of three-agent intelligent systems [1], [2]. The intelligent agent has several interfaces: a messenger-based chatbot, a mobile … B. Relation Extraction To extract relations between entities, we use improved methods, the basic versions of which have already shown the …

Deep Reinforcement Learning for Text and Speech
U Kamath, J Liu, J Whitaker – Deep Learning for NLP and Speech …, 2019 – Springer
… In real domains, however, it takes very large amounts of labeled data to learn to perform high quality extraction. Furthermore, relation extraction quality depends on the results of entity extraction (and vice versa) … 13.8 Entity extraction with DQN. 13.4.1.2 Relation Extraction …

A survey on question answering systems over linked data and documents
E Dimitrakis, K Sgontzos, Y Tzitzikas – Journal of Intelligent Information …, 2019 – Springer
… We can distinguish such systems to chatbots which are used mainly for fun (starting from the 1966 system ELIZA 1966) and dialogue agents which are goal/task ori … 1) Question Analysis Answer Type Classification Answer Type Recognition Relation Extraction Focus Recognition …

Learning to generate questions by learningwhat not to generate
B Liu, M Zhao, D Niu, K Lai, Y He, H Wei… – The World Wide Web …, 2019 – dl.acm.org
… Besides, question generation is also important in conversational systems and chatbots such as Siri, Cortana, Alexa and Google Assistant, helping … language processing tasks, including semantic role labeling [23], document matching [20, 39], relation extraction [40], and so on …

Political Campaigns, Social Media, and Analytics: The Case of the GDPR
N Dimisianos – Politics and Technology in the Post-Truth Era, 2019 – emerald.com
… Information extraction is further broken into two other processes, namely entity recognition (ER) and relation extraction (RE) … Chatbots, digital avatars, and of course the Internet of Things are some of the existing technologies which are very likely to be leveraged very soon for …

Efficient Algorithm for Answering Fact-based Queries Using Relational Data Enriched by Context-Based Embeddings
AA Altowayan – 2019 – webpage.pace.edu
… Intelligent conversational systems – such as question answering and chatbots – are becoming a more critical component of today’s AI in areas … NLP tech- niques, such as Named Entity Recognition and Relation Extraction, are applied in which we propose a new approach suited …

AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue
G Kumar, R Joshi, J Singh, P Yenigalla – arXiv preprint arXiv:1912.10160, 2019 – arxiv.org
… Conver- sational agents can be broadly classified into two categories: a task oriented chat bot and a chit-chat based system respectively … S. Jat, S. Khandelwal, and P. Talukdar. Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention …

Viana: Visual interactive annotation of argumentation
F Sperrle, R Sevastjanova, R Kehlbeck… – … IEEE Conference on …, 2019 – ieeexplore.ieee.org
… is a flourishing research area that enables various novel, linguistically-informed applications like semantic search en- gines, chatbots or human … users have gained an overview of the corpus at hand, they progress to the Text View for locution identification and relation extraction …

A Bert Based Relation Classfication Network for Inter-Personal Relationship Extraction
C Peng – CCKS2019-shared task, 2019 – conference.bj.bcebos.com
… Relation extraction is a sub problem of information extraction, it’s target is to extract relation between a pair of given entities from … to many Articial Intelligence(AI) applications, such as Information Retrieval(IR), Intelligent Question and Answering(QA), and Intelligence Chat-bots …

How data science workers work with data: Discovery, capture, curation, design, creation
M Muller, I Lange, D Wang, D Piorkowski… – Proceedings of the …, 2019 – dl.acm.org
… I-02 M Team lead Business analytics I-03 M Lead data scientist Sales I-04 M Model builder Transportation I-05 M Applied AI researcher Education I-06 M Model builder Healthcare I-07 M Applied AI researcher Information technology I-08 M Applied AI researcher Chatbot I-09 M …

Memory grounded conversational reasoning
S Moon, P Shah, R Subba, A Kumar – Proceedings of the 2019 …, 2019 – aclweb.org
… Page 2. 146 Figure 2: Memory Walker Chatbot UI for memory grounded conversations between a user and the assistant. Figure … Zhao. 2016. Question answering on freebase via relation extraction and textual evidence. ACL …

A hybrid retrieval-generation neural conversation model
L Yang, J Hu, M Qiu, C Qu, J Gao, WB Croft… – Proceedings of the 28th …, 2019 – dl.acm.org
Page 1. A Hybrid Retrieval-Generation Neural Conversation Model Liu Yang ?1 Junjie Hu2 Minghui Qiu3 Chen Qu 1 Jianfeng Gao4 W. Bruce Croft1 Xiaodong Liu4 Yelong Shen5 Jingjing Liu4 1 Center for Intelligent Information …

Multi-disciplinary Trends in Artificial Intelligence: 13th International Conference, MIWAI 2019, Kuala Lumpur, Malaysia, November 17–19, 2019, Proceedings
R Chamchong, KW Wong – 2019 – books.google.com
… Contents Regular Papers Text Relation Extraction Using Sentence-Relation Semantic Similarity … 255 Boldizsár Tü?-Szabó, Péter Földesi, and László T. Kóczy Identification of Conversational Intent Pattern Using Pattern-Growth Technique for Academic Chatbot …

I-Know: Knowledge Representation and Manipulation for Self-aware Robots
N Naeem – 2019 – dspace.cuilahore.edu.pk
… promising results but still, there are some gaps such as some systems are based on verb extraction, they neglect the nouns and adjective, similarly, some are noun-mediated … in chatbot to do conversations in natural language; to decide the next execution plan …

Multi-disciplinary Trends in Artificial Intelligence
R Chamchong, KW Wong – Springer
… Contents Regular Papers Text Relation Extraction Using Sentence-Relation Semantic Similarity … 255 Boldizsár Tü?-Szabó, Péter Földesi, and László T. Kóczy Identification of Conversational Intent Pattern Using Pattern-Growth Technique for Academic Chatbot …

Augmenting advanced analytics into enterprise systems: A focus on post-implementation activities
A Elragal, HED Hassanien – Systems, 2019 – mdpi.com
… solutions. Additionally, the illustration shows how the purposefully designed spoken dialogue system (SDS) or chatbot helps users to manually interact with the AAE for problem identification and solution-confirmation purposes …

Combining neural networks and pattern matching for ontology mining-a meta learning inspired approach
D Roussinov, N Puchnina – 2019 IEEE 13th International …, 2019 – ieeexplore.ieee.org
… extremely effective in several natural language appli- cations, such as machine translation, sentiment analysis, parsing, and chatbots … Natural language processing tasks such as parsing, relation extraction, anaphora and co-reference resolution also benefit from knowing the …

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
… many Artificial Intelligence (AI) applications, such as Information Retrieval (IR), Intelligent Question and Answering (QA), and Intelligence Chat-bots (IC … 1. Bekoulis, G., Deleu, J., Demeester, T., Develder, C.: Joint entity recognition and relation extraction as a multi-head selection …

Dynamic transfer learning for named entity recognition
P Bhatia, K Arumae, EB Celikkaya – International Workshop on Health …, 2019 – Springer
… NER is an important application as an information extraction tool for downstream tasks such as entity linking [7] and relation extraction [26 … For future work, we plan to explore our model on other sequential problems such as translation, summarization, chat bots as well as explore …

A context-aware conversational agent in the rehabilitation domain
T Mavropoulos, G Meditskos, S Symeonidis… – Future Internet, 2019 – mdpi.com
… assistants has been reported in Reference [10], where emphasis is placed on the role of chatbots beyond patient monitoring; it focuses on patient–doctor interaction over a chatbot-driven telemedicine platform after the former has been discharged from a clinical environment …

An Improved Word Representation for Deep Learning Based NER in Indian Languages
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Named Entity Recognition (NER) is the process of identifying the elementary units in a text document and classifying them into predefined categories such as person, location, organization and so forth. NER plays an important role in many Natural Language Processing applications …

An end-to-end generative architecture for paraphrase generation
Q Yang, D Shen, Y Cheng, W Wang, G Wang… – Proceedings of the …, 2019 – aclweb.org
… For example, paraphrases can help diversify the response of chatbot engines (Yan et al., 2016), strengthen question answering (Harabagiu and Hickl, 2006; Duboue and Chu- Carroll, 2006; Fader et al., 2014), augment relation extraction (Romano et al., 2006), and extend the …

SSN_NLP at SemEval-2019 Task 3: Contextual Emotion Identification from Textual Conversation using Seq2Seq Deep Neural Network
D Thenmozhi, A Chandrabose… – Proceedings of the 13th …, 2019 – aclweb.org
… This helps conversational agents, chat bots and messengers to avoid emotional cues and mis- communications by detecting the emotions during … Ssn nlp@ iecsil-fire-2018: Deep learning approach to named entity recognition and relation extraction for conversational systems in …

Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings
S Isotani, E Millán, A Ogan, P Hastings, B McLaren… – 2019 – books.google.com
Page 1. Seiji Isotani· Eva Millán · Amy Ogan · Peter Hastings· Bruce McLaren · Rose Luckin (Eds.) Artificial Intelligence in Education 20th International Conference, AIED 2019 Chicago, IL, USA, June 25–29, 2019 Proceedings, Part I 123 Page 2 …

Out-of-Domain Detection for Low-Resource Text Classification Tasks
M Tan, Y Yu, H Wang, D Wang, S Potdar… – arXiv preprint arXiv …, 2019 – arxiv.org
… son Assistant1. For example, Table 1 shows some of the utterances a chat-bot builder provided for training … studies suggests that few-shot learning is promis- ing in the text domain, including text classifica- tion (Yu et al., 2018; Jiang et al., 2018), relation extraction (Han et al …

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language …
K Inui, J Jiang, V Ng, X Wan – Proceedings of the 2019 Conference on …, 2019 – aclweb.org
Page 1. EMNLP-IJCNLP 2019 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing Proceedings of the Conference November 3–7, 2019 Hong Kong, China Page 2 …

Natural Language Processing, Understanding, and Generation
A Singh, K Ramasubramanian, S Shivam – Building an Enterprise Chatbot, 2019 – Springer
… human-like conversation. Figure 5-1 shows an architecture that utilizes the techniques from NLP, NLU, and NLG to build an enterprise chatbot. Open image in new window Figure 5-1. Figure 5-1 Architecture diagram for chatbots …

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 error … 10, 417–424 (2013) [7] JY Lee, F. Dernoncourt, P. Szolovits, “Mit at semeval-2017 task 10: relation extraction with convolutional …

Data science in healthcare: Benefits, challenges and opportunities
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… to interact with the general population directly, eg via crowdsourcing, analysing search logs (http://blogs.microsoft.com/next/ 2016/06/07/how-web-search-data-might-help-diagnose-serious- illness-earlier/# sm.0001mr81jwowvcp6zs81tmj7zmo81) or AI-based chatbots, are ways …

Dialogue breakdown detection robust to variations in annotators and dialogue systems
J Takayama, E Nomoto, Y Arase – Computer Speech & Language, 2019 – Elsevier
… For the variationality, different chat-bots show different characteristics in their responses. DBDC data consists of dialogues collected from three systems: a chat-bot API provided by … Recent studies have applied the attention mechanism to CNN for relation extraction (Wang, Cao …

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
A Korhonen, D Traum, L Màrquez – … of the 57th Annual Meeting of the …, 2019 – aclweb.org
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Prediction and decision-making in intelligent environments supported by knowledge graphs, a systematic review
E Amador-Domínguez, E Serrano, D Manrique… – Sensors, 2019 – mdpi.com
… Its use has been successfully tested on chatbots, enabling their understanding and generation of human-like responses. Nonetheless, the usage of KBs in intelligent environments changes qualitatively across the different existing domains of application …

How Data Science Workers Work with Data
M Muller, I Lange, D Wang, D Piorkowski… – S. Brewster, G …, 2019 – researchgate.net
… I-02 M Team lead Business analytics I-03 M Lead data scientist Sales I-04 M Model builder Transportation I-05 M Applied AI researcher Education I-06 M Model builder Healthcare I-07 M Applied AI researcher Information technology I-08 M Applied AI researcher Chatbot I-09 M …

ICTAI 2019
YM Boumarafi, Y Salhi – computer.org
… State University, USA),NLP / NLU – 4,,Impact of Argument Type and Concerns in Argumentation with a Chatbot , 1549, Lisa … TJ Watson Research Center), and Shimei Pan (University of, Maryland, Baltimore County),NLP / NLU – 5,,Enhancing Relation Extraction Using Syntactic …

Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning
J Wang, C Sun, S Li, J Wang, L Si, M Zhang… – arXiv preprint arXiv …, 2019 – arxiv.org
Page 1. Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning Jingjing Wang1, Changlong Sun2, Shoushan Li1,? , Jiancheng Wang1, Luo Si2, Min Zhang1 …

Towards Fast and Unified Transfer Learning Architectures for Sequence Labeling
P Bhatia, K Arumae, B Celikkaya – 2019 18th IEEE …, 2019 – ieeexplore.ieee.org
… is an important application as an information extraction tool for downstream tasks such as entity linking [7] and relation extraction [8]. Medical text … For future work, we plan to explore our model on other sequential problems such as translation, summarization, chat bots as well as …

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 …

Extending knowledge graphs with subjective influence networks for personalized fashion
K Bollacker, N Díaz-Rodríguez, X Li – Designing Cognitive Cities, 2019 – Springer
… is in (Novalija and Leban 2013), which exploits lexico-syntactic patterns as NLP tools for ontology learning, relation extraction and curation … Based on text-based interfaces, the bloom of chat bots is unstoppable (in Facebook Messenger, Slack, etc.); however, these are merely for …

Anemone: a Visual Semantic Graph
J Ficapal Vila – 2019 – diva-portal.org
… al. propose a complex sentence-level attention- based model for relation extraction [35] … The kind of model that we use could be seen as a chatbot that is plugged to a time series autoencoder to output words that are syntactically cor- rect but also preserve the meaning of the …

Folksonomy Based Question Answering System
S Ramaswamy – 2019 – utd-ir.tdl.org
Page 1. FOLKSONOMY BASED QUESTION ANSWERING SYSTEM by Swetha Ramaswamy APPROVED BY SUPERVISORY COMMITTEE: _____ Dr. Dan Moldovan, Chair …

Example-Driven Question Answering
D Wang – 2019 – lti.cs.cmu.edu
… 95 B Chatbots Interview: bJFK vs bNixon 97 C Chatbots Interview: bStarWars vs bTrump vs bHillary 109 Bibliography 115 x Page 11. List of Figures 1.1 Workflow Diagram of the Example-driven Question Answering . . . . . 6 …

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 …

The Semantic Web: 16th International Conference, ESWC 2019, Portorož, Slovenia, June 2–6, 2019, Proceedings
P Hitzler, M Fernández, K Janowicz, A Zaveri… – 2019 – books.google.com
Page 1. Pascal Hitzler· Miriam Fernández· Krzysztof Janowicz· Amrapali Zaveri· Alasdair JG Gray· Vanessa Lopez· Armin Haller · Karl Hammar (Eds.) The Semantic Web 16th International Conference, ESWC 2019 Portorož, Slovenia, June 2–6, 2019 Proceedings 123 Page 2 …

Towards Literate Artificial Intelligence
M Sachan – 2019 – ml.cmu.edu
Page 1. Towards Literate Artificial Intelligence Mrinmaya Sachan June 2019 CMU-ML-19-110 Machine Learning Department School of Computer Science Carnegie Mellon University Pittsburgh, PA Thesis Committee Eric P. Xing, Chair Jaime Carbonell Tom Mitchell Dan Roth …

Natural Language Processing and Chinese Computing: 8th CCF International Conference, NLPCC 2019, Dunhuang, China, October 9–14, 2019 …
J Tang, MY Kan, D Zhao, S Li, H Zan – 2019 – books.google.com
Page 1. Jie Tang· Min-Yen Kan · Dongyan Zhao · Sujian Li· Hongying Zan (Eds.) Natural Language Processing and Chinese Computing 8th CCF International Conference, NLPCC 2019 Dunhuang, China, October 9–14, 2019 Proceedings, Part I 123 Page 2 …

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 …

Decompositional Semantics for Events, Participants, and Scripts in Text
R Rudinger – 2019 – jscholarship.library.jhu.edu
… What’s new? Human: I’ve eaten nothing all day. AI: How did it taste? Figure 1.1: An example of an award-winning chatbot, “Mitsuku,” failing to respond appropriately to a human user. (Inappropriate responses in red italics.) https: //www.pandorabots.com/mitsuku …

Developing Cognitive Bots Using the IBM Watson Engine
W Platform – Springer
… She has implemented and worked on chat bots using various cognitive engines, including Watson, LUIS … scale due to the advent of cognitive virtual assistants—more commonly known as chatbots. A chatbot is a computer program that converses in natural language via auditory …

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 …

Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives
J Xu, P Yang, S Xue, B Sharma, M Sanchez-Martin… – Human Genetics, 2019 – Springer
… Autonomous vehicles, smart homes, chat bots, individualized marketing, fraud detection, and high-frequency automated trading are some examples of AI empowering humans to live in a more efficient and personal- ized way …

Semantic and Discursive Representation for Natural Language Understanding
D Sileo – 2019 – tel.archives-ouvertes.fr
… for their automatic fulfilment (as in chatbot systems, robotics or information retrieval). Automated analysis of text can allow humans (or other agents, as in algorithmic trading) to learn new insights, such as sentiment analy- sis, summarization, relation extraction, trends detection …

BERT for Open-Domain Conversation Modeling
X Zhao, Y Zhang, W Guo, X Yuan – 2019 IEEE 5th International …, 2019 – ieeexplore.ieee.org
… applied to question answering, text classification, text augmentation, text representation, text generation evaluation, reading comprehension, relation extraction and semantic … 2015) [7] Sato, S., Yoshinaga, N., Toyoda, M., Kitsuregawa, M.: Modeling situations in neural chat bots …

Augmenting MPI Programming Process with Cognitive Computing
P Kazilas – 2019 – diva-portal.org
… written regular expression patterns. Theses patterns can either be hand-written or extracted by relation extraction methods. • The N-gram tiling or redundancy-based approach is used only for searches in the Web. After the return …

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 …

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 …

CA5211 C PROGRAMMING AND DATA STRUCTURES LABORATORY LTPC
P PO – DEPARTMENT OF INFORMATION SCIENCE AND … – management.ind.in
Page 38. CA5211 C PROGRAMMING AND DATA STRUCTURES LABORATORY LTPC 0 0 4 2 OBJECTIVES: • To introduce the concepts of structured programming language. • To develop skills in design and implementation of data structures and their applications …

RM5151 RESEARCH METHODOLOGY AND IPR LT PC
P PO – M. TECH. INFORMATION TECHNOLOGY … – cac.annauniv.edu
Page 22. 22 PO1 PO2 PO3 PO4 PO5 PO6 CO1 2 1 1 3 1 1 CO2 3 1 2 3 2 2 CO3 3 1 2 3 2 2 CO4 2 2 2 3 2 2 CO5 2 3 2 2 1 2 CO6 3 2 3 2 1 2 RM5151 RESEARCH METHODOLOGY AND IPR LT PC 2 0 0 2 OBJECTIVES: To …

Bibliography on Weihrauch complexity
V Brattka, DD Dzhafarov, A Marcone… – Dagstuhl Reports, Vol …, 2019 – drops.dagstuhl.de
Page 23. Vasco Brattka, Damir D. Dzhafarov, Alberto Marcone, and Arno Pauly 21 6 Bibliography on Weihrauch Complexity For an always up-to-date version of this bibliography see http://cca-net. de/publications/ weibib. php …

Artificial intelligence in financial services: an analysis of the AI technology and the potential applications, implications, and risks it may propagate in financial …
R Mardanghom, H Sandal – 2019 – openaccess.nhh.no
… 37 3.3 Chatbots and Robotic Advisory Services …………… 38 … We will in particular dig into algorithmic trading, cyber security, fraud detection and compliance, chatbots and robotic advisory services, and accounting and auditing …

Weakly Supervised Machine Learning for Cyberbullying Detection
E Raisi – 2019 – vtechworks.lib.vt.edu
Page 1. Weakly Supervised Machine Learning for Cyberbullying Detection Elaheh Raisi Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of …

Lojbanic English, An Interlingua for Parallel Machine Translation
LP Immes – 2019 – search.proquest.com
Page 1. LOJBANIC ENGLISH, AN INTERLINGUA FOR PARALLEL MACHINE TRANSLATION A Dissertation Presented by LUKE P. IMMES Submitted to the Graduate School of the University of Massachusetts Lowell in partial fulfillment of the requirements for the degree of …

Using Machine Learning and Graph Mining Approaches to Improve Software Requirements Quality: An Empirical Investigation
M Singh – 2019 – search.proquest.com
… Using NLP, programmers can structure knowledge to perform many language processing tasks eg translation, semantic relation extraction, speech recognition … [41]), and development of Chat bots (a deep learning model that interacts with humans) [42] …