Notes:
Naive Bayes is a machine learning algorithm that is commonly used in natural language processing (NLP) tasks, including chatbot development. It is based on the idea of using Bayes’ theorem to make predictions about the likelihood of certain events occurring, based on the presence or absence of certain features.
In the context of chatbots, Naive Bayes can be used to classify user messages into predefined categories or intents. For example, a chatbot might use Naive Bayes to classify user messages as being related to weather, sports, news, or some other topic. To do this, the chatbot would need to be trained on a dataset of user messages, along with labels indicating the intent of each message. The chatbot would then use this training data to learn the features that are associated with each intent, and use these features to make predictions about the intent of new, unseen messages.
Naive Bayes is often used in chatbot development because it is a simple and effective algorithm that can perform well on a wide range of NLP tasks. It is particularly well-suited to classification tasks, where the goal is to assign a label or category to a given input. However, it is important to note that Naive Bayes can be prone to making errors when applied to certain types of data, and may not always perform as well as more complex algorithms on more challenging tasks.
See also:
100 Best Bayesian Tutorial Videos
Arabic Dialogue Act Recognition for Textual Chatbot Systems
A Joukhadar, H Saghergy, L Kweider… – Proceedings of The First …, 2019 – aclweb.org
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EH Yilmaz, C Toraman – Proceedings of the 43rd International ACM …, 2020 – dl.acm.org
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AK Jena, A Sinha, R Agarwal – Proceedings of the Second Workshop on …, 2020 – aclweb.org
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SA ABIR – 2019 – researchgate.net
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F Delahunty, M Arcan, R Johansson – 2019 – thesiscommons.org
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J Schuurmans, F Frasincar – IEEE Intelligent Systems, 2019 – ieeexplore.ieee.org
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… There is a huge range of chatbots identified based on the learning capacity and the manner of interaction … Porter used the Naïve Bayes supervised learning approach … Z., Duan, N., Bao, J., Chen, P., Zhou, M.: DocBot: an information retrieval approach for chatbot engines using …
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SR Duggenpudi, KSS Varma, R Mamidi – … of the 2nd Workshop on Deep …, 2019 – aclweb.org
… 68.778% Naive Bayes+BOW+Unigrams 86.046% Naive Bayes+TFIDF+Unigrams 83.721% Naive Bayes+TFIDF+Bigrams 83.721% Naive Bayes+TFIDF+Both … Spoken dialogue system using recognition of user’s feedback for rhythmic dialogue … A neural-network based chat bot …
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J Romero, S Razniewski, K Pal, J Z. Pan… – Proceedings of the 28th …, 2019 – dl.acm.org
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W Cai, L Chen – Proceedings of the 28th ACM Conference on User …, 2020 – dl.acm.org
… authors identified four most-frequent user intents in a shopping chatbot (ie, recommendation … they may want to achieve when they interact with a dialogue system [15] … text classification [1], including Logistic Regression, Support Vector Machine (SVM), Naive Bayes, Decision Tree …
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HE Cherng, CH Chang – NTCIR14. p. to appear, 2019 – research.nii.ac.jp
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J Lopez – TALN-RECITAL 2019-PFIA 2019, 2019 – hal.archives-ouvertes.fr
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MA Kilian, M Kattenbeck, M Ferstl… – Aslib Journal of …, 2019 – emerald.com
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N Lokeswari, S Priyanka, K Amaravathi – ijresm.com
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HE Cherng, CH Chang – arXiv preprint arXiv:1907.03070, 2019 – arxiv.org
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P Cañas Castellanos – 2020 – e-archivo.uc3m.es
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A Partovi, I Zukerman – Proceedings of the 20th Annual SIGdial Meeting …, 2019 – aclweb.org
… We describe a longitudinal user study con- ducted in the context of a Spoken Dialogue System for a … (2016) produced dialogue contributions of chat- bots; and Serban et al … types from the corpora collected in Stage 1 of our experiment (Section 3):7 Naïve Bayes, Support Vector …
A Novel Multi-agent-based Chatbot Approach to Orchestrate Conversational Assistants
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… in mobile devices and smart speakers, educational tutoring agents, entertainment chatbots in open … 2004) and has been used to develop hundreds of successful commercial dialogue systems … based approaches are also an efficient alternative when the dialogue system must be …
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T Saha, S Srivastava, M Firdaus, S Saha… – … Joint Conference on …, 2019 – ieeexplore.ieee.org
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Q Xie, D Tan, T Zhu, Q Zhang, S Xiao… – … IEEE Conference on …, 2019 – ieeexplore.ieee.org
… Chatbot knows the answer only because the input is in the associated pattern. Similarly, chatbots respond to anything related to the associated patterns, but it cannot go beyond the associated pattern … Naive Bayes is the classic algorithm for text classification …
Towards the Learning, Perception, and Effectiveness of Teachable Conversational Agents
N Chhibber – 2019 – uwspace.uwaterloo.ca
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Chatbot Interaction with Artificial Intelligence: Human Data Augmentation with T5 and Language Transformer Ensemble for Text Classification
JJ Bird, A Ekárt, DR Faria – arXiv preprint arXiv:2010.05990, 2020 – arxiv.org
… October 14, 2020 ABSTRACT In this work, we present the Chatbot Interaction with Artificial Intelligence (CI-AI) framework as an approach to the training of deep learning chatbots for task classification. The intelligent system …
Artificial Intelligence for Text Analytics
MY Day – 2020 – ntpu.edu.tw
… 16 2020/12/31 (Question Answering and Dialogue Systems) 17 2021/01/07 I (Final Project Presentation I) … Entropy (ME) Naïve Bayes (NB) … Bot Maturity Model 54 Source: https://www. capgemini.com/2017/04/how-can-chatbots-meet-expectations-introducing-the-bot-maturity …
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UK Premasundera, MC Farook – 2019 19th International …, 2019 – ieeexplore.ieee.org
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A Saleh, N Jaques, A Ghandeharioun, J Shen… – Proceedings of the …, 2020 – ojs.aaai.org
… A successful open- domain dialog system could provide enormous value en- abling more natural … also unlock new, beneficial applications of AI, such as companion chatbots for therapy … model for producing toxic responses as determined by a Naive Bayes- Logistic Regression …
Estimating Conversational Styles in Conversational Microtask Crowdsourcing
S Qiu, U Gadiraju, A Bozzon – Proceedings of the ACM on Human …, 2020 – dl.acm.org
… called Guardian, which enables both expert and non-expert workers to collaboratively translate Web APIs into a dialogue system format [22] … A previous work based on Facebook Messenger used a Chatbot to connect learners and experts, for providing experts’ feedback to …
Chichang Jou
MY Day – 2020 – mail.tku.edu.tw
… 15 2020/06/08 (Question Answering and Dialogue Systems) 16 2020/06/15 I (Final Project Presentation I) … Entropy (ME) Naïve Bayes (NB) … Bot Maturity Model 52 Source: https://www. capgemini.com/2017/04/how-can-chatbots-meet-expectations-introducing-the-bot-maturity …
Developing an LSTM-based Classification Model of IndiHome Customer Feedbacks
A Arifianto, S Suyanto, A Sirwan… – … Conference on Data …, 2020 – ieeexplore.ieee.org
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… Even though modern chatbots employ … Better the prediction model accuracy using comparison-based methods, Naïve Bayes and decision tree, the dialogue patterns from different tutors could be varied … Tangkathach [10] presented the learning portal system for the chatbot …
Socially-Aware Dialogue System
R Zhao – 2019 – lti.cs.cmu.edu
… However, social chatbots fall short in replicating the interpersonal function of communication … SAPA) This chapter reviews our knowledge-inspired socially-aware dialogue system in a … recognition of conversational strategies in the service of a socially-aware dialog system …
Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems
S Vajjala, B Majumder, A Gupta, H Surana – 2020 – books.google.com
… Pipeline Using Existing Text Classification APIs One Pipeline, Many Classifiers Naive Bayes Classifier Logistic … A Simple FAQ Bot A Taxonomy of Chatbots Goal-Oriented Dialog Chitchats A … for Building Dialog Systems Dialog Systems in Detail PizzaStop Chatbot Deep Dive …
Review on: Virtual Assistant and Patient Monitoring System by using AI & Data Science
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… Keywords-: Artificial intelligence(AI), Data Science, Machine Learning; Naive Bayes algorithm for behavior … system uses Natural language processing, Machine learning for creating a chatbot … a. Methodology: This system uses the multi modal dialogue system which processes …
Latest Developments in Deep Learning in Finance 8th November 2019
NYU Courant – 2019 – pdfs.semanticscholar.org
… CLASSIFICATION Discriminant Analysis Naïve Bayes Nearest Neighbors CART … Machine translation • Spoken dialog systems • Complex question answering NLP in Industry … Speech recognition • Chatbots / Dialog agents • Automating customer support • Controlling devices …
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E Merdivan – 2019 – tel.archives-ouvertes.fr
… utilisant des mod`eles statistiques et standardisés d’apprentissage de machine tels que Naive Bayes, Hidden Markov Models (HMM) et Conditional Random Fields (CRF) … and developing intelligent dialogue systems for AAL systems, with an emphasis on a …
Towards computational persuasion via natural language argumentation dialogues
A Hunter, L Chalaguine, T Czernuszenko… – Joint German/Austrian …, 2019 – Springer
… of an argument), we can train a classifier (eg a classification tree, a naive Bayes classifier, or … The other aspect of developing argumentation chatbots for persuasion is to hook-up the interface to … which the user selects his/her choice to be replaced by the chatbot natural language …
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 …
AIA-BDE: A Corpus of FAQs in Portuguese and their Variations
HG Oliveira, J Ferreira, J Santos, P Fialho… – Proceedings of The …, 2020 – aclweb.org
… oriented questions and their variations, ready to be used in the evaluation of IR / QA dialogue systems … it could be used as plan B in a single-turn QA dialogue system: when no … Classifiers were learned with three different methods, namely a Linear SVM, a Naïve Bayes (NB) and …
Artificial Intelligence acceptance: morphological elements of the acceptance of Artificial Intelligence
MM Figueiredo – 2019 – repositorio.ucp.pt
… Some examples of supervised learning algorithms are logistic regression, naive bayes, random forest and artificial neural networks4 … Duolingo is a very well-known chatbot that allows people to learn and … to chatbots ranging from virtual agents and dialogue systems to machine …
Speech Command Classification System for Sinhala Language based on Automatic Speech Recognition
T Dinushika, L Kavmini… – … Conference on Asian …, 2019 – ieeexplore.ieee.org
… this speech command classification system and develop a speech dialog system for the … Setyawan, RM Awangga and SR Efendi, “Comparison Of Multinomial Naive Bayes Algorithm And Logistic Regression For Intent Classification In Chatbot,” International Conference on …
Named Entity Recognition and Relation Detection for Biomedical Information Extraction
N Perera, M Dehmer, F Emmert-Streib – Frontiers in Cell and …, 2020 – frontiersin.org
The number of scientific publications in the literature is steadily growing, containing our knowledge in the biomedical, health, and clinical sciences. Since there is currently no automatic archiving of the obtained results, much of this information remains buried in textual details not …
Transforming the communication between citizens and government through AI-guided chatbots
A Androutsopoulou, N Karacapilidis, E Loukis… – Government Information …, 2019 – Elsevier
… on various well-tried algorithms and techniques, including Neural Networks, K-means, Decision Trees, Naïve Bayes, and Support … blocks, the abovementioned services add intelligence to the functionality and user interfaces of existing chatbots (and chatbot builders), the …
InPHYNet: Leveraging attention-based multitask recurrent networks for multi-label physics text classification
V Udandarao, A Agarwal, A Gupta, T Chakraborty – Knowledge-Based Systems – Elsevier
… to-use form that can be utilized to build a question-answering physics chatbot, we prepared … an increasing focus on building end-to-end QA based interactive dialog systems for facilitating … of work has been pivoted on the Student Response Analysis (SRA) part of a dialog system …
Cap. 1. ARTIFICIAL INTELLIGENCE APPLICATIONS AND TOOLS IN HIGHER EDUCATION: AN OVERVIEW
N Romanov, MI Cluci, ID Anastasiei… – … educa?iei doctorale în … – sesyr.feaa.uaic.ro
… which could be related to personalized learning, students’ personal data, chat bots and even … a foreign language more efficiently with the use of human-machine dialogue systems … Tree (CART) algorithms, JRIP Decision Rule, Gradient Boosting Trees and NB (Naive Bayes) …
Adaptive and Personalized Systems Based on Semantics
P Lops, C Musto, F Narducci, G Semeraro – Semantics in Adaptive and …, 2019 – Springer
… Synset-based user profiles were learnt using a relevance feedback algorithm based on the Rocchio method [114] or a naïve Bayes classifier [69], and those models are able to classify new items as interesting or not with respect to the user preferences …
Detecting Abuse on the Internet: It’s Subtle
S Bagga – 2020 – search.proquest.com
Page 1. Detecting Abuse on the Internet: It’s Subtle Sunyam Bagga Master of Science School of Computer Science McGill University Montreal, Quebec December, 2019 A thesis submitted to McGill University in partial fulfillment …
Comparison and efficacy of synergistic intelligent tutoring systems with human physiological response
F Alqahtani, N Ramzan – Sensors, 2019 – mdpi.com
The analysis of physiological signals is ubiquitous in health and medical diagnosis as a primary tool for investigation and inquiry. Physiological signals are now being widely used for psychological and social fields. They have found promising application in the field of computer …
Computational Sarcasm Processing: A survey
KGP Bhattacharyya – cfilt.iitb.ac.in
… a sarcasm generation module (Sar- casmBot) for chatbots and mention that integrat- ing a sarcasm generation module allows existing chatbots to become … to train a variety of classi- fiers including linear SVM, logistic regression, de- cision tree, random forest and naive bayes …
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research
S Poria, D Hazarika, N Majumder… – arXiv preprint arXiv …, 2020 – arxiv.org
… Sentiment- aware dialogue system Sentiment- aware style transfer … and unsupervised–have employed myriad of algorithms that include SVMs (Moraes et al., 2013a), Naive Bayes Clas- sifiers … 2.3 presents one such example where a user is chatting with a chit-chat style chatbot …
Recognition of cooking activities through air quality sensor data for supporting food journaling
F Gerina, SM Massa, F Moi… – Human-centric …, 2020 – hcis-journal.springeropen.com
… Indeed, we believe that these technologies, possibly coupled with automatic dialog systems, may provide adequate solutions for unobtrusive and privacy-conscious context recognition, as well as user engagement for supporting diet data acquisition on the long term …
Machine Learning from Casual Conversation
A Mohammed Ali – 2019 – stars.library.ucf.edu
… 105 Figure 6.2: Training and Testing Naïve Bayes Classifier, Decision Tree Classifier (DT) … There is a long history of studying conversational agents and chatbots. The earliest known chatbot was ELIZA [132], which was designed to emulate a Rogerian therapist …
“I think it might help if we multiply, and not add”: Detecting Indirectness in Conversation
P Goel, Y Matsuyama, M Madaio, J Cassell – … on Spoken Dialogue System …, 2019 – Springer
… following supervised machine learning algorithms—Logistic Regression, Naive Bayes, Random Forest … or a general-purpose socially-aware spoken dialogue system [30] that … in interpersonal communication, incorporating its detection in spoken dialogue systems may ultimately …
Towards inclusive education in the age of artificial intelligence: Perspectives, challenges, and opportunities
PS Mohammed – Artificial intelligence and inclusive education, 2019 – Springer
… In addition, computer-supported collaborative learning (CSCL) and dialogue systems were actively pursued particularly in feedback systems … (2016) used Naïve Bayes classifiers to … Home (a speaker and voice assistant), Cozmo (a robot toy) and Julie (a chatbot) affected the …
Conversational summarization for chatbots in a banking environment
M de Ruiter – 2020 – researchgate.net
… of Conversationality Conversationality is an important aspect of text summariza- tion in the context of chatbots … replies are selected, since they are mostly well written, and because the chatbot’s purpose is … The experimental setup uses a Naive Bayes (NB) classifier and an LSTM …
User intent prediction in information-seeking conversations
C Qu, L Yang, WB Croft, Y Zhang, JR Trippas… – Proceedings of the 2019 …, 2019 – dl.acm.org
… can only be extracted from the metadata of the forum, such as user authority level or answer votes, so that our method can be applicable to dialog systems … We adopt classic ML methods, including Naive Bayes classifier, SVM, random forest, and AdaBoost as baseline classifiers …
You talkin’to me? A practical attention-aware embodied agent
RR Divekar, JO Kephart, X Mou, L Chen… – IFIP Conference on Human …, 2019 – Springer
… data to characteristics of the conversation and trained and evaluated a Naive Bayes classifier … Radziwill and Benton (2017) have proposed a good approach to evaluating chatbots, involving evaluation … here was not to evaluate the conversation or capability of the chatbot as a …
Computational Sarcasm for Different Languages: A Survey
A Dubey, A Joshi, P Bhattacharyya – cfilt.iitb.ac.in
… They present a sarcasm genera- tion module (SarcasmBot) for chatbots and mention that integrating a sarcasm generation module allows existing chatbots to become more ‘human’. 4 Datasets In this section, we describe datasets for computational sar- casm … Naive Bayes …
Developing Amaia: A Conversational Agent for Helping Portuguese Entrepreneurs—An Extensive Exploration of Question-Matching Approaches for Portuguese
J Santos, L Duarte, J Ferreira, A Alves, HG Oliveira – Information, 2020 – mdpi.com
… 2. Related Work. Dialogue systems typically exploit large collections of text, often including conversations … In opposition to generative systems, IR-based dialogue systems do not handle very well requests for which there is no similar text in the corpus …
Adaptace jazykového modelu na téma v reálném ?ase
J Lehe?ka – 2019 – otik.uk.zcu.cz
… days. Typical applications, in which ASR is indispensable, are: (1) chatbots, virtual assis- tants and dialogue systems, where people are conversing with computers, (2) dictating systems, where people need to note a large amount of information without typing it …
A data-efficient deep learning approach for deployable multimodal social robots
H Cuayáhuitl – Neurocomputing, 2020 – Elsevier
… First, dialogue acts are derived from the most likely actions, Pr(a|s) > 0.001, with probabilities derived from a Naive Bayes classifier trained from example dialogues—see [1]. Second, all game moves were allowed from the subset of those not taken yet (to the robot’s knowledge) …
Diving Deep into Deep Learning: History, Evolution, Types and Applications
HCA Deekshith Shetty, MJ Varma, S Navi, MR Ahmed – researchgate.net
… Polynomial Regression Support Vector Machine (SVM) Support Vector Regression Kernel SVM Decision Tree Regression Naive Bayes Decision Tree Classification Random Forest Classification Unsupervised Learning Clustering Dimensionality Reduction K-Means Principal …
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 …
Medical Instructed Real-Time Assistant for Patient with Glaucoma and Diabetic Conditions
UU Rehman, DJ Chang, Y Jung, U Akhtar… – Applied Sciences, 2020 – mdpi.com
… calls, and many more things [1]. Many applications from different domains currently have their own built-in virtual assistants such as televisions [2], mobile devices [3], vehicles [4], and the Internet of things [5,6]. The virtual assistant is also known as a chatbot, dialogue manager …
Artificial Intelligence in the legal sector. A comparative analysis of expert and AI approaches to predicting court decisions
N Kaliazina – 2019 – pdfs.semanticscholar.org
… ones such as spell checking, keyword search, information extraction from websites, documents classification, machine translation, spoken dialogue systems, and complex … classification algorithms (for example, support vector machines (SVM) or Naive Bayes) … chatbots that …
AI Enabled Foreign Language Immersion: Technology and Method to Acquire Foreign Languages with AI in Immersive Virtual Worlds
RR Divekar – 2020 – search.proquest.com
Page 1. AI ENABLED FOREIGN LANGUAGE IMMERSION: TECHNOLOGY AND METHOD TO ACQUIRE FOREIGN LANGUAGES WITH AI IN IMMERSIVE VIRTUAL WORLDS Rahul R. Divekar Submitted in Partial Fulfillment of the Requirements for the Degree of …
Recommendation in Dialogue Systems
Y Sun – 2019 – escholarship.org
… These chatbots are implemented on different platforms, such as mobiles, home devices, and webpages … method, the dialogue system, and their intersections. Chapter 3 introduces the … Another example is the machine learning based Naive Bayes approach [74] [69] …
LREC 2020 Workshop Language Resources and Evaluation Conference 11–16 May 2020
A Bhatia, S Shaikh – lrec2020.lrec-conf.org
… Goldwasser, 2019). Analysis of chatbot responses (Prakhar Gupta and Bigham, 2019) yields human-judgement correla- tion improvements. Approaches above differ from ours in that they require extensive model training. Our …
Critical Infrastructures Security: Improving Defense Against Novel Malware and Advanced Persistent Threats
G Laurenza – 2020 – iris.uniroma1.it
Page 1. Critical Infrastructures Security: Improving Defense Against Novel Malware and Advanced Persistent Threats Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza – University of Rome …
A Review on Dyadic Conversation Visualizations-Purposes, Data, Lens of Analysis
JY Kim, RA Calvo, K Yacef, NJ Enfield – arXiv preprint arXiv:1905.00653, 2019 – arxiv.org
… Lastly, the nature of human-to-bot conversation can al- so be different from human-to-human conversations. In a general, unconstrained context, current dialogue systems are incapable of generating responses that are rated as highly appropriate by humans [45] …
Era of Intelligent Systems in Healthcare
S Belciug, F Gorunescu – … Decision Support Systems—A Journey to …, 2020 – Springer
The aim of this chapter is to prepare the reader for the outstanding trip that she/he embarked when starting reading this book. At first, we shall try to look for answers to some of the most…
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 … The ELIZA chatbot (Weizenbaum 1976) or contestants to the Loeb- ner Prize competition (Stephens … valency assessment, predicting the quantity of silence required for a dialog system to take …
Intelligent Decision Support Systems: A Journey to Smarter Healthcare
S Belciug, F Gorunescu – 2020 – Springer
Page 1. Intelligent Systems Reference Library 157 Smaranda Belciug Florin Gorunescu Intelligent Decision Support Systems–A Journey to Smarter Healthcare Page 2. Intelligent Systems Reference Library Volume 157 Series Editors …
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 …
ICTAI 2019
YM Boumarafi, Y Salhi – computer.org
Page 1. 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) ICTAI 2019 Table of Contents General Chair’s Foreword xxxvi Message from the ICTAI Program Chair xxxvii ICTAI 2019 Conference Committees xxxviii Program Area Chairs xxxix …
A Companion Robot for Modeling the Expressive Behavior of Persons with Parkinson’s Disease
AP Valenti – 2020 – search.proquest.com
… 107 Chapter 6 Improving Natural Language Understanding in Spoken Dialogue Systems 109 6.1 Introduction … xix Page 21. 6.1 Typical components of a spoken dialogue system. At each turn t, input speech is converted to an utterance, ut, which the Natural Lan …
Cognitive Computing Recipes
A Masood, A Hashmi – Springer
Page 1. Cognitive Computing Recipes Artificial Intelligence Solutions Using Microsoft Cognitive Services and TensorFlow — Adnan Masood Adnan Hashmi Foreword by Matt Winkler Page 2. Cognitive Computing Recipes Artificial Intelligence Solutions Using …
A Systematic Approach for Automatically Answering General-Purpose Objective and Subjective Questions
LP Acharya – 2019 – repository.lib.fit.edu
… 1960s by the MIT Artificial Intelligence Laboratory to demonstrate the communication between humans and machines. Similar to a chatbot, ELIZA uses pattern matching and substitution methodologies to simulate conversations. DOCTOR is an example of a script …
DEEP NEURAL NETWORD BASED NATURAL LANGUAGE INFERENCE MODEL
T BEKELE – 2020 – ir.bdu.edu.et
… NLU services in different domains and for different purposes, eg natural language inference [23], question answering for localized search [24], form driven dialogue systems [25], dialogue management [26], and the internet of things [27]. 2.2 …
Machine learning in mental health: A systematic review of the HCI literature to support the development of effective and implementable ML systems
A Thieme, D Belgrave… – ACM Trans. Comput …, 2020 – designandwellbeing.com
Page 1. 34 Machine Learning in Mental Health: A Systematic Review of the HCI Literature to Support the Development of Effective and Implementable ML Systems ANJA THIEME and DANIELLE BELGRAVE, Microsoft Research GAVIN DOHERTY, Trinity College Dublin …
Machine Learning in Mental Health: A Systematic Review of the HCI Literature to Support the Development of Effective and Implementable ML Systems
A Thieme, D Belgrave, G Doherty – ACM Transactions on Computer …, 2020 – dl.acm.org
Page 1. 34 Machine Learning in Mental Health: A Systematic Review of the HCI Literature to Support the Development of Effective and Implementable ML Systems ANJA THIEME and DANIELLE BELGRAVE, Microsoft Research GAVIN DOHERTY, Trinity College Dublin …