Maximum Entropy & Chatbots

Notes:

Maximum entropy is a statistical concept that refers to the state of maximum uncertainty or randomness within a system. In the context of chatbots, maximum entropy can be used as a measure of the uncertainty or lack of information about a particular topic or conversation.

In chatbots, maximum entropy can be used as a way to identify when a chatbot does not have sufficient information to provide a reliable or accurate response to a user’s input. For example, if a chatbot is asked a question about a topic that it has not been trained on, or if it receives ambiguous or incomplete input, it may have a high level of entropy, indicating that it is uncertain about how to respond.

One way that maximum entropy can be used in chatbots is as a way to trigger a fallback or default response when the chatbot is unable to provide a more specific or accurate response. For example, a chatbot might have a fallback response of “I’m sorry, I’m not sure how to help with that” when it has a high level of entropy.

In addition to being used as a measure of uncertainty, maximum entropy can also be used as a optimization goal in machine learning and AI systems, where it can be used to find the most probable distribution of data given a set of constraints or assumptions.

Resources:

Wikipedia:

References:

See also:

100 Best Apache OpenNLP Videos | Apache OpenNLP & Dialog Systems

A Compression-based BiLSTM for Treating Teenagers’ Depression Chatbot
J YIN – DEStech Transactions on Computer Science and …, 2019 – dpi-proceedings.com
… than those trained from Wikipedia and news, but is not necessarily better at performance [6]. NLP systems can be built on a maximum entropy approach based on … [2] Abdul-Kader SA, Woods J C. Survey on chatbot design techniques … [3] Skjuve M, Brandtzæg P B. Chatbots as a …

Is the Simplest Chatbot Effective in English Writing Learning Assistance?
R Nagata, T Hashiguchi, D Sadoun – International Conference of the …, 2019 – Springer
… Table 7. Bi-grams starting with definite article. Chatbot. Standard Editor. Bi-gram. # Occurrences … 169–176 (2008)Google Scholar. 5. Han, NR, Chodorow, M., Leacock, C.: Detecting errors in English article usage with a maximum entropy classifier trained on a large, diverse corpus …

Chatbot for digital marketing and customer support: an artificial intelligence approach
HJA Vaz – 2019 – repositorium.sdum.uminho.pt
… Maximum Entropy Markov Model or MEMM is a statistical model based on Hidden Markov Models (HMMs) that uses contextual features to predict … 2.3 chatbots A Chatbot is a computer program that can interact with a human using natural language with textual or auditory inputs …

Modeling Machine Learning Agent for Interaction Conversational System Using Max Entropy Approach in Natural
AK Negi, SI Hassan – Data Communication and Networks …, 2019 – books.google.com
… Modeling Machine Learning Agent… 219 1.1 Maximum Entropy Overview In a service model which deliver the services like email, network, VPN, etc. There is a call center interface required where user come and interact with machine learning chatbot for registering his …

JAQ: a chatbot for foreign students
A Gellens, S Gustin, Y Deville – dial.uclouvain.be
… As the research question states, the produced chatbot can serve as a Proof Of Concept demon- strating that modern chatbots are suited in the context we described earlier. It can be tested at http://tfe-gustin-gellens.info.ucl.ac.be inside the network of UCLouvain …

Dialogue generation: From imitation learning to inverse reinforcement learning
Z Li, J Kiseleva, M de Rijke – Proceedings of the AAAI Conference on …, 2019 – aaai.org
… 2017). There are two broad di- rections for training a dialogue generation system: the first employs defined rules or templates to construct possible re- sponses and the second builds a chatbot to learn the response … Maximum entropy inverse reinforcement learning (MaxEnt-IRL) …

Way off-policy batch deep reinforcement learning of implicit human preferences in dialog
N Jaques, A Ghandeharioun, JH Shen… – arXiv preprint arXiv …, 2019 – arxiv.org
… Soft Q-learning motivates using a Boltzmann distribution in the value function as a way of performing maximum entropy RL [21]. KL-control has also been used to improve transfer learning between maximum likelihood estimation (MLE) training on data, and training with RL [28] …

Identifying facts for chatbot’s question answering via sequence labelling using recurrent neural networks
M Nuruzzaman, OK Hussain – Proceedings of the ACM Turing …, 2019 – dl.acm.org
… that not yet solved completely [1]. In recent years, both academic and industry showed interests in chatbot, which is … The most common sequence labelling models are Hidden Markov Model (HMM), Maximum Entropy Markov Model (MEMM), Conditional Random Fields (CRF …

A big data approach to examining social bots on Twitter
X Liu – Journal of Services Marketing, 2019 – emerald.com
… Moreover, sentiment analysis and the maximum entropy algorithm were applied to automatically extract consumer sentiment from big textual data with … age, the rising trends of providing superior customer experiences include using artificial intelligence systems, chat bots or big …

Recommendence and fashionsence: Online fashion advisor for offline experience
R Chakraborty, K Vats, K Baradia, T Khan… – Proceedings of the …, 2019 – dl.acm.org
… in the maximum possible way for the online world using a combination of chatbots and machine … extensible in the future to a mobile app) front-end and the conversational chatbot persona is … the next best attribute is identified as look since it has the maximum entropy among all …

TEXT ANALYTICS METHODS FOR SENTENCE-LEVEL SENTIMENT ANALYSIS
N Zou – 2019 – trepo.tuni.fi
… methods in sentiment analysis include Neural Networks, Naive Bayes classification, Support Vector Machines and Maximum Entropy classification [46] … Many talking machines named Chatbot, such as Alexa developed by Amazon, can manage complicated interactions with …

A morpho-syntactically informed lstm-crf model for named entity recognition
L Simeonova, K Simov, P Osenova, P Nakov – arXiv preprint arXiv …, 2019 – arxiv.org
… Natural Language Processing (NLP) tasks such as Ques- tion Answering, Information Extraction, Machine Translation, Dialog Systems, and chatbots, where it is … (2013) reported pre- cision of 88.27, recall of 78.00, and F1 score of 82.82 using a Maximum Entropy Markov Model …

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
… help chat bots have cold-to-start or continue conversations with human users [9]. Most of the existing question generation models for English are based on a sequence-to-sequence model … maximum entropy classifier to identify the named entity in a declarative sentence …

Artificial intelligence tools for smart tourism development
T Gajdošík, M Marciš – Computer Science On-line Conference, 2019 – Springer
… From ML techniques it uses Stanford’s maximum entropy classifier to classify tourists’ reviews … Another example is Bold360, an AI-integrated chatbot and live-agent software that uses natural language … Chatbots, Sam, Kayak, Bold360, Booking.com’s Assistant, Mezi, Hipmunk …

A study of incorrect paraphrases in crowdsourced user utterances
MA Yaghoub-Zadeh-Fard, B Benatallah… – Proceedings of the …, 2019 – aclweb.org
… Also known as dialogue systems, virtual assistants, chatbots or simply bots (Campagna et al., 2017; Su et al., 2017), some advanced bots are now designed to perform complex tasks (eg, flight booking), many of which are built using machine learning tech- niques …

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 coherent responses than standard encoder-decoder mod- els … the brand Kellogg’s?” we are asking, “Which brand was it?” We select the food attribute with the maximum entropy at each …

EmoChat: Bringing multimodal emotion detection to mobile conversation
L Chong, M Jin, Y He – … Conference on Big Data Computing and …, 2019 – ieeexplore.ieee.org
… Similarly, emotion information is also indispensable for the chatbot systems like Siri [1] and Xiao Ice [2]. In these systems, emotion … For example, Bo Pang et al. [23] apply three machine learning methods (Naive Bayes, maximum entropy classification, and support vector …

Vietnamese Question Classification based on Deep Learning for Educational Support System
BT Hung – 2019 19th International Symposium on …, 2019 – ieeexplore.ieee.org
… There are many different classification methods used such as: Support Vector Machine, Naive Bayesian classification, Maximum Entropy Models [1] [7] [12]. Nguyen et al … 319 Page 4. Fig.5 LINDA Chatbot for Educational Support System …

Comparison of Named Entity Recognition Tools Applied to News Articles
S Vychegzhanin, E Kotelnikov – 2019 Ivannikov Ispras Open …, 2019 – ieeexplore.ieee.org
… The BiLSTM-CRF model is used for Named Entity Recognition. 7. DeepPavlov [10] – a framework for chatbots and virtual assistants development, written in Python … NLTK Python Apache License v2.0 Maximum Entropy – ACE …

Semantic vector learning for natural language understanding
S Jung – Computer Speech & Language, 2019 – Elsevier
… 1. Introduction. Natural language understanding (NLU) is a central technique to implement natural user interfaces such as chatbot, mobile secretary, and smart speakers. The goal of NLU is to extract meanings from natural language and infer user intention …

CLINICAL MEDICAL KNOWLEDGE EXTRACTION USING CROWDSOURCING TECHNIQUES
D Aswini – 2019 – academia.edu
… c) The proposed method is further demonstrated by building the chatbot which can suggest the possible disease by raised the certain … The extract 13 non-textual features from each question-answer pair and train a maximum entropy model to classify the unlabeled question …

Automated Image Captioning for Flickr8K Dataset
KA Kumari, C Mouneeshwari, RB Udhaya… – … Conference on Artificial …, 2019 – Springer
… Deep learning is an emerging technology to develop various products like image recognition software, self-driving car, recommender systems, chatbots, etc … The word detector outputs serve as conditional inputs to a maximum-entropy language model [15] …

A comparison study: Comparison Between Different Classifiers for Emotion Classification Using Real Human-to-Human Chat Log Dataset
Z Zhao – 2019 – cdr.lib.unc.edu
… One possible result of implementation of multi-class classification is that chatbots can provide users with better communication experience which gives warmer and friendlier atmosphere … Page 8. 5 Rao et al. (2016) proposed a topic-level maximum entropy (TME) model for social …

Reinforcement learning for personalized dialogue management
F Den Hengst, M Hoogendoorn… – IEEE/WIC/ACM …, 2019 – dl.acm.org
Page 1. Reinforcement Learning for Personalized Dialogue Management Floris den Hengst ING Groep NV Floris.den.Hengst@ing.com Mark Hoogendoorn Vrije Universiteit Amsterdam M.Hoogendoorn@vu.nl Frank van Harmelen …

Recovering dropped pronouns in Chinese conversations via modeling their referents
J Yang, J Tong, S Li, S Gao, J Guo, N Xue – arXiv preprint arXiv …, 2019 – arxiv.org
… (2018) also presented a hierarchical recurrent attention network (HRAN) to model the hierarchy of conversation context in order to generate multi- turn responses in chatbots … MEPR: This model is provided by Yang (2015), which uses a maximum entropy (ME) classifier with …

Improving NLTK for processing Portuguese
J Ferreira, H Gonçalo Oliveira… – 8th Symposium on …, 2019 – drops.dagstuhl.de
… Funding This work was funded by FCT’s INCoDe 2030 initiative, in the scope of the demonstration project AIA, “Apoio Inteligente a Empreendedores (Chatbots)” … NLTK provides a few different models for PoS tagging, one of which is based on Maximum Entropy …

Survey on Out-Of-Domain Detection for Dialog Systems
YS Jeong, YM Kim – Journal of Convergence for Information …, 2019 – koreascience.or.kr
… desirable services by just saying their needs to intelligent conversational agent, which is also typically called as chatbot or dialog … model (LDM) [28], latent semantic analysis (LSA) [29], support vector machines (SVM) [13], logistic regression [30], maximum entropy model (MEM …

A review of deep learning based speech synthesis
Y Ning, S He, Z Wu, C Xing, LJ Zhang – Applied Sciences, 2019 – mdpi.com
… which have been utilized to enhance a wide range of application scenarios such as intelligent speech interaction, chatbot or conversational … contain only one-layer nonlinear transformation units, such as hidden Markov models (HMMs) [7,8] and maximum Entropy (MaxEnt) [9 …

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
… knowledge for personalization in downstream applications such as Web-based chatbots and agents … General-purpose chatbot-like agents show decent performance in benchmarks (eg, [13, 20, 37 … I work for Microsoft as a software engineer”, using maximum-entropy classifiers [15 …

Robust Neural Machine Translation for Clean and Noisy Speech Transcripts
MA Di Gangi, R Enyedi, A Brusadin… – arXiv preprint arXiv …, 2019 – arxiv.org
… 2005, pp. 57–64. [Online]. Available: https://www.aclweb.org/anthology/W05-0908 [28] P.-J. Chen, I.-H. Hsu, Y.-Y. Huang, and H.-Y. Lee, “Mitigating the impact of speech recognition errors on chatbot using sequence-to … [33] J. Huang and G. Zweig, “Maximum entropy model for …

Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach
Z Zhao, X Ma – Proceedings of the 2019 Conference on Empirical …, 2019 – aclweb.org
… widely used applications such as classifying e-commerce products reviews (Rao et al., 2016) and devel- oping emotionally intelligent chatbots for health … In terms of EDL, two differ- ent models have been proposed recently, includ- ing a maximum entropy model (Zhou et al., 2016 …

End to End Parts of Speech Tagging and Named Entity Recognition in Bangla Language
JR Saurav, S Haque… – … Conference on Bangla …, 2019 – ieeexplore.ieee.org
… Solving these kinds of fundamental tasks will help to develop a more advanced system like a chatbot, optimize search results, etc … They showed the use of CRFs, Maximum Entropy(ME), Support Vector Machine(SVM)s and achieved f1 measure varies from 82% to 91% on the …

Social-Media Sentiment, Limited Attention, and Stock Returns
WS Leung, WK Wong, G Wong – Limited Attention, and Stock …, 2019 – papers.ssrn.com
… 6 FastText have been used heavily by Facebook in chatbots, marketing ads. For example, in chatbots application it learns the sentiment from what user typed, classifying the sentence to correct category and show required result …

The application of sentiment analysis and text analytics to customer experience reviews to understand what customers are really saying
C Gallagher, E Furey, K Curran – International Journal of Data …, 2019 – igi-global.com
… customeranalyticstodayisbetter thaneverbefore.Datasourcessuchascustomersatisfactionsurveys, socialmediachannels,chatbots, voicecalls … XoccursgiventheevidenceYis determinedbyBayes ruleshowninEquation(1).UnlikeNaïveBayes,MaximumEntropyClassifier doesnot …

Detection of Common English Grammar Usage Errors
L Immes, H Levkowitz – International Conference: Beyond Databases …, 2019 – Springer
… [17] use a maximum entropy machine learning algorithm to detect errors in non-native speakers of English—prepositions in particular. Precision was \(84\%\), recall was \(19\%\) … Another TensorFlow application, [14], uses chatbots to generate intelligent English text …

Estimating Aggressiveness of Russian Texts by Means of Machine Learning
D Levonevskiy, D Malov, I Vatamaniuk – International Conference on …, 2019 – Springer
… Naive Bayes, Maximum Entropy, Support Vector Machines (SVM) … Such results can be used to estimate the aggressiveness probability, for example, to rank social network messages for subsequent manual verification or to adjust the chatbot behavior models …

Deep Learning for Opinion Mining: A Systematic Survey
Y Agarwal, R Katarya… – 2019 4th International …, 2019 – ieeexplore.ieee.org
… A different approach to mine opinions from big data in [11] as it uses a deep learning model called “SS-BED (Sentiment and Semantic-Based Emotion Detector).” They also develop a chatbot that can … “Support Vector Machines, Naïve Bayes, maximum entropy” algorithms can …

Incremental processing of noisy user utterances in the spoken language understanding task
S Constantin, J Niehues, A Waibel – arXiv preprint arXiv:1909.13790, 2019 – arxiv.org
Page 1. Incremental processing of noisy user utterances in the spoken language understanding task Stefan Constantin? Jan Niehues+ Alex Waibel? ? Karlsruhe Institute of Technology Institute for Anthropomatics and Robotic {stefan.constantin|waibel}@kit.edu …

NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution
A Khandelwal, S Sawant – arXiv preprint arXiv:1911.04211, 2019 – arxiv.org
… Analysis, where the sentiment of a given sentence is dependent on negation, to query response systems like Chatbots, where negation … They proposed an MRS Crawler, and a maximum entropy model for parse ranking, trained on a different dataset of encyclopedia articles and …

A multi-task hierarchical approach for intent detection and slot filling
M Firdaus, A Kumar, A Ekbal… – Knowledge-Based Systems, 2019 – Elsevier
… handling intent and slot together, we can build an end-to-end natural language understanding (NLU) module for any task-oriented chatbot … the heterogeneous features of an utterance was used for intent detection in [9]. The authors in [10], used maximum entropy classifiers for …

Estimating Aggressiveness of Russian Texts by Means of Machine Learning
I Vatamaniuk – … , SPECOM 2019, Istanbul, Turkey, August 20–25 …, 2019 – books.google.com
… com/Tweets classification (positive/negative/neutral) Naive Bayes, Maximum Entropy, Support Vector Machines (SVM) uClassify Provides a set of … for example, to rank social network messages for subsequent manual verification or to adjust the chatbot behavior models …

Imitation learning from imperfect demonstration
YH Wu, N Charoenphakdee, H Bao… – arXiv preprint arXiv …, 2019 – arxiv.org
… In contrast, AL approaches learn from interacting with environments and optimize objectives such as maximum entropy [Ziebart et al., 2008]. A state-of-the-art approach generative adversarial imitation learning (GAIL) is proposed by Ho and Ermon [2016] …

Building Pedagogical Conversational Agents, Affectively Correct.
M Feidakis, P Kasnesis, E Giatraki, C Giannousis… – CSEDU (1 …, 2019 – researchgate.net
… In education, Chatbots have been recently applied to various disciplines such as physics … For instance, through machine learning-oriented Q&As, a Chatbot is able to … Vector Machines, Deep Neural Networks, Naïve Bayes, Bayesian Network and Maximum Entropy MaxEnt, with …

A Deep Learning Model with Data Enrichment for Intent Detection and Slot Filling
S Dadas, J Protasiewicz… – 2019 IEEE International …, 2019 – ieeexplore.ieee.org
… in numerous practical applications, including human- computer interfaces, call centre automation (IVR systems), chat bots, and semantic … were generally combinations of heuristics and statistical models with conditional random fields (CRF) and maximum entropy Markov models …

A hybrid convolutional and recurrent network approach for conversational AI in spoken language understanding
IMTL Douai – Fourth Conference on Software Engineering and …, 2019 – seim-conf.org
… users queries when communicating with the users in a natural way throw these chatbots … Popular approaches to solving sequence labeling problems include maximum entropy Markov models (MEMMs)[4], conditional … But still, in the w ay to implement a full chatbot, we will need …

Customizable text generation via conditional text generative adversarial network
J Chen, Y Wu, C Jia, H Zheng, G Huang – Neurocomputing, 2019 – Elsevier
JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …

Utility Learning, Non-Markovian Planning, and Task-Oriented Programming Language
N Shukla – 2019 – escholarship.org
… Under the same framework, we also show how a virtual chat-bot may learn dialogue policies from few example transcripts, resulting in an interpretable dialogue model, outperforming current statistical techniques … 59 5.2.3 Cognitive Actions (Applied to Chat-bot Selling Items) …

ProductQnA: Answering user questions on e-commerce product pages
A Kulkarni, K Mehta, S Garg, V Bansal… – … Proceedings of The …, 2019 – dl.acm.org
… KEYWORDS question answering; deep learning; chatbot; e-commerce ACM Reference Format: Ashish Kulkarni, Kartik Mehta, Shweta Garg … search, recommendation, information extraction, summarization, and re- cently, automatic question answering [17, 20] and chatbots [22] …

Automatic Question Answering System for College Use
G Singal, R Bagri, A Das – 2019 – ir.juit.ac.in
… Because it is one of the Instant Messenger that offers the functionality to make chat bots. ? One of the most secure Instant Messenger … Page 29. 22 The testdb.py code Create a new file called testdb.py in the same directory as your Chatbot script, and add the following code …

Data Science and Conversational Interfaces: A New Revolution in Digital Business
D Griol, Z Callejas – Data Science and Digital Business, 2019 – Springer
… In a survey from 2016, 80% of business leaders stated that they either “already used or planned to use Chatbots by 2020” when asked which technologies they had … Maximum Entropy (ME): This approach is a method of estimating a probability distribution from given data …

An Approach to Cluster Tamil Literatures Using Discourse Connectives
R Anita, CN Subalalitha – 2019 IEEE 1st International …, 2019 – ieeexplore.ieee.org
… proposed improvisation of implicit discourse relations through the classification of explicit discourse connectives using Maximum Entropy classifiers [11 … of which various semantic representation could be built such as Question Answering systems, Chatbots, Information Retrieval …

A Proposal for the Development of Lifelong Dialog Systems
D Griol, A Sanchis, JM Molina – … Workshop on Soft Computing Models in …, 2019 – Springer
… The design practices of conventional commercial chatbots are currently well established in industry … The conditional distributions shown in Eqs. (4) and (5) can be estimated by means of the general technique of choosing the maximum entropy (MaxEnt) distribution that properly …

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
… The criterion was maximizing the cosine distance to the bag-of-words repre- sentations of the predefined intent classes or with classi- fiers such as an SVM [1], [2] and maximum-entropy classi- fier [3]. In such bag-of-words systems, the relations between words were given by a …

Contributions to Clinical Information Extraction in Portuguese: Corpora, Named Entity Recognition, Word Embeddings
FAC Lopes – 2019 – estudogeral.sib.uc.pt
… 9 2.4 Example of label bias problem using Maximum Entropy Markov Mod- els (MEMM). Image extracted from https://cocoxu.github.io … xvii, xviii, 3, 15, 16, 17, 24, 27, 28, 47, 52, 53, 54, 67 ME Maximum Entropy. 10, 11, 12 MEMM Maximum Entropy Markov Models …

Reverse-engineering satire, or “paper on computational humor accepted despite making serious advances”
R West, E Horvitz – Proceedings of the AAAI Conference on Artificial …, 2019 – aaai.org
… As computers increasingly take on conversational tasks (eg, in chat bots and personal assistants), the ability to interact with users naturally … We use OpenNLP’s maximum entropy chunker (Berger, Pietra, and Pietra 1996), after retraining it to better handle pithy, headline-style text …

Deep Learning for Textual Emotion Mining
S Chawla, M Mehrotra – International Conference on Futuristic Trends in …, 2019 – Springer
… Some of the most frequently used classifiers include Support Vector Machines (SVM), Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), Conditional Random Field (CRF), Multinomial Naïve Bayes (MNB) and Maximum Entropy (MaxEnt) …

Contextual speech recognition with difficult negative training examples
U Alon, G Pundak, TN Sainath – ICASSP 2019-2019 IEEE …, 2019 – ieeexplore.ieee.org
… We refer to the Songs, Contacts and Talk-To as “contextualized test sets”, as these contain context relevant for recognition (eg, contact names, songs names and chatbot names) … 1, pp. 2442–2452. [13] Adwait Ratnaparkhi, “A maximum entropy model for part- of-speech tagging …

Sentiment Analysis in Social Networks: A Methodology Based on the Latent Dirichlet Allocation Approach
F Clarizia, F Colace, F Pascale… – 11th Conference of …, 2019 – atlantis-press.com
… In [30] three machine learning approaches (Na?ve Bayes, Maximum Entropy and Support Vector Machines) has been adopted to label the polarity of a movie reviews datasets … [8] F. Clarizia, F. Colace, M. Lombardi, F. Pascale, and D. Santaniello, Chatbot: An education support …

A comparative analysis of machine comprehension using deep learning models in code-mixed hindi language
S Viswanathan, MA Kumar, KP Soman – Recent Advances in …, 2019 – Springer
… Question classification is another important aspect of traditional QA systems which deals with categorizing the type of questions [1]. One of the major application of QA system are conversational models referred as chat-bots, it has a vital role in business perspective as they …

Recommendation in Dialogue Systems
Y Sun – 2019 – escholarship.org
… Google Assistant, the Facebook Messenger and the Cortana of Microsoft. These chatbots are implemented on different platforms, such as mobiles, home devices, and webpages. Dialogue systems are becoming indispensable tools in our life. First, the di …

Design feasibility of an automated, machine-learning based feedback system for motivational interviewing.
ZE Imel, BT Pace, CS Soma, M Tanana, T Hirsch… – …, 2019 – psycnet.apa.org
… For example, a text-based chatbot client could interact with a trainee therapist and provide instant feedback on every therapist statement … https://doi.org/10.2196/10001: Berger, AL, Pietra, VJD, & Pietra, SAD (1996). A maximum entropy approach to natural language processing …

Situated interaction
D Bohus, E Horvitz – The Handbook of Multimodal-Multisensor Interfaces …, 2019 – dl.acm.org
… The earliest attempts at dialog between computers and people were text-based dialog systems, such as Eliza [Weizenbaum 1966], a pattern- matching chat-bot that emulated a psychotherapist, and SHRLDU [Winograd 1971], a natural language understanding system that …

Construction Safety Informatics
RYM Li – 2019 – Springer
… 117 6.7 AI Chatbot for Filling in the Accident Report and Smart Concrete Sensor … The construction industry, however, has gone one step further to develop a chatbot which can assist safety officers in filling out their safety reports …

Sentiment Analysis through Transfer Learning for Turkish Language
SE Akin, T Yildiz – … on INnovations in Intelligent SysTems and …, 2019 – ieeexplore.ieee.org
… social media and compares the performance of Naive Bayes, Maximum Entropy and Support … to boost the performance of reinforcement learning- based goal-oriented chatbots for restaurant … C. Musat, A. Hossman, and M. Baeriswyl, “Goal-Oriented Chatbot Dialog Management …

An Urdu semantic tagger-lexicons, corpora, methods and tools
J Shafi – 2019 – eprints.lancs.ac.uk
… LLOCE Longman Lexicon of Contemporary English MaEn Maximum Entropy MD Muaz Dataset … engineering [182], historical semantic analysis via HTST 1.1 [166], to train a Chatbot [218], and several others [23, 214]. Moreover, USAS [180] has been ported previously …

Challenge discussion: advancing multimodal dialogue
J Allen, E André, PR Cohen, D Hakkani-Tür… – The Handbook of …, 2019 – dl.acm.org
… We could talk about all of these questions for a long time, so let’s move on to the next topic, which is Chatbots. Now of course many people mean very many different things by the term “chatbot,” but essentially what I am referring to here are end-to-end trained systems that …

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 … The ELIZA chatbot (Weizenbaum 1976) or contestants to the Loeb- ner Prize competition (Stephens 2004) are … Another landmark approach was maximum entropy (Berger et al …

Pretrained language model transfer on neural named entity recognition in Indonesian conversational texts
R Leonandya, F Ikhwantri – arXiv preprint arXiv:1902.07938, 2019 – arxiv.org
… The former is a 16K conversational mes- sages from users having small talk with a chatbot, whereas the latter contains 12K task-oriented messages such as movie tickets … In: EMNLP (2010) 7. Curran, JR, Clark, S.: Language independent ner using a maximum entropy tagger …

Generating Descriptive and Accurate Image Captions with Neural Networks
L Wu – 2019 – opus.lib.uts.edu.au
… multiple-stage methods. In [20], Fang et al. used multiple instance learning to train visual detectors for words that commonly occur in captions, and then developed a model to generate sentences with these words through maximum-entropy training. In [3], Hendricks et al …

Asking the right questions about the psychology of human inquiry: Nine open challenges
A Coenen, JD Nelson, TM Gureckis – Psychonomic Bulletin & Review, 2019 – Springer
The ability to act on the world with the goal of gaining information is core to human adaptability and intelligence. Perhaps the most successful and influential account of such abilities is the…

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 …

Food Ordering System based on Human Computer Interaction and Machine Learning Techniques
A Sajnani, N Patel – Asian Journal For Convergence In Technology …, 2019 – asianssr.org
… 5. Seq2Seq as chatbot [7 … Various Machine Learning Algorithms which are usually used for classification of text are: Maximum Entropy, Nave Bayes, Support vector machines … seq2seq [7] wildml [Online] Available: http://www.wildml.com/2016/04/deep- learning-for-chatbots-part-1 …

Learning to Converse With Latent Actions
T Zhao – 2019 – lti.cs.cmu.edu
… speech processing, Professor Alan W Black who has taught me machine translation, Professor Ruslan Salakhutdinov who has taught me about Variational Autoencoders and Professor Roni Rosenfeld who has taught me language models and the principle of maximum entropy …

TUM Data Innovation Lab
H Agarwala, R Becker, M Fatima, L Riediger, A Belitski… – 2019 – di-lab.tum.de
… and commercial models for the purpose of developing chat bots, more specifically ”goal-oriented chat bots” … The potential is quite big as developers could improve their chatbots over time … provides the flexibility to modify the pipeline and deploy the entire chatbot from personal …

Multimodal integration for interactive conversational systems
M Johnston – The Handbook of Multimodal-Multisensor Interfaces …, 2019 – dl.acm.org
Page 1. IPART MULTIMODAL LANGUAGE AND DIALOGUE PROCESSING Page 2. Page 3. 1Multimodal Integration for Interactive Conversational Systems Michael Johnston 1.1 Introduction This chapter discusses the challenges …

From words to pixels: text and image mining methods for service research
FV Ordenes, S Zhang – Journal of Service Management, 2019 – emerald.com
… As such, according to a Gartner (2018) report, business analytics tools that can deal with text and image data, such as natural language processing, computer vision, machine learning, deep neuronal nets and chatbots are of critical interest to firms …

Mobile Technology for Gamification of Natural Language Grammar Acquisition
M Purgina – 2019 – u-aizu.repo.nii.ac.jp
… outcomes. The equivalent of ChemCollective in language learning would be a virtual character (chatbot), able to discuss a range of predefined topics or engage in a free dialog with the user, and provide different kinds of feedback …

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 …

Applications In Sentiment Analysis And Machine Learning For Identifying Public Health Variables Across Social Media
EM Clark – 2019 – scholarworks.uvm.edu
… outcomes and then analyze all patient tweets separately from the general public. Maximum Entropy Logistic regression content classifiers, (Genkin et al., 2007), convert sentences from a text to word vectors – called the vocabulary of the classifier …

The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors
TM Fonseka, V Bhat… – Australian & New Zealand …, 2019 – journals.sagepub.com
Objective:Suicide is a growing public health concern with a global prevalence of approximately 800000 deaths per year. The current process of evaluating suicide risk is highly subjective, which ca…

Adaptace jazykového modelu na téma v reálném ?ase
J Lehe?ka – 2019 – otik.uk.zcu.cz
… work in human-to-computer interactions, which are increasingly important in present 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 …

A multimodal approach to sarcasm detection on social media
D Das – 2019 – researchgate.net
… of sarcasm Page 89 7.3 Top five objects that were closest to the gaze center points Page 90 8.1 Sample positive and negative reviews, and replies from chatbot-based auto-replier sys- tem. Page 94 8.2 Inappropriate response from auto-replier for a multimodal sarcastic review …

Survey on publicly available sinhala natural language processing tools and research
N de Silva – arXiv preprint arXiv:1906.02358, 2019 – arxiv.org
Page 1. 1 Survey on Publicly Available Sinhala Natural Language Processing Tools and Research Nisansa de Silva Abstract— Sinhala is the native language of the Sinhalese people who make up the largest ethnic group of Sri Lanka …

Ambient Assisted Living with Deep Learning
E Merdivan – 2019 – tel.archives-ouvertes.fr
Page 1. HAL Id: tel-02927785 https://tel.archives-ouvertes.fr/tel-02927785 Submitted on 2 Sep 2020 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not …

Doménov?-specifická adaptace NER
B Jakovcheski – 2019 – dspace.cvut.cz
… 6 Page 27. 1.1. Background OpenNLP also included maximum entropy and perceptron based machine learning … 1.1.2.6 Chatbot NER Chatbot NER14 is heuristic based that uses several NLP techniques to extract necessary entities from chat interface …

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 …

Commercialization of multimodal systems
PR Cohen, R Tumuluri – The Handbook of Multimodal-Multisensor …, 2019 – dl.acm.org
Page 1. 15Commercialization of Multimodal Systems Philip R. Cohen, Raj Tumuluri 15.1 Introduction This chapter surveys the broad and accelerating commercial activity in build- ing products incorporating multimodal-multisensor interfaces …

Multimodal databases
M Valstar – The Handbook of Multimodal-Multisensor Interfaces …, 2019 – dl.acm.org
Page 1. 10Multimodal Databases Michel Valstar 10.1 Introduction In the preceding chapters, we have seen many examples of Multimodal, Multisen- sor Interfaces (MMIs). Almost all of these interfaces are implemented as computer …

TOPIC MODELLING, SENTIMENT ANALSYS AND CLASSIFICATION OF SHORT-FORM TEXT
CJOFI PURCHASES, L STOYANOVA, W WALLACE – 2019 – local.cis.strath.ac.uk
Page 1. TOPIC MODELLING, SENTIMENT ANALSYS AND CLASSIFICATION OF SHORT-FORM TEXT CUSTOMER JOURNEY OF INSURANCE PURCHASES RESEARCHER LAZARINA STOYANOVA CHIEF INVESTIGATOR WILLIAM WALLACE …

Multimodal conversational interaction with robots
G Skantze, J Gustafson, J Beskow – The Handbook of Multimodal …, 2019 – dl.acm.org
Page 1. 2Multimodal Conversational Interaction with Robots Gabriel Skantze, Joakim Gustafson, Jonas Beskow 2.1 Introduction Being able to communicate with machines through spoken interaction has been a long-standing vision in both science fiction and research labs …

Automotive multimodal human-machine interface
D Schnelle-Walka, S Radomski – The Handbook of Multimodal …, 2019 – dl.acm.org
Page 1. 12Automotive Multimodal Human-Machine Interface Dirk Schnelle-Walka, Stefan Radomski 12.1 Introduction The majority of user interfaces in the automotive domain were not developed as the result of user-centered …

Augmenting MPI Programming Process with Cognitive Computing
P Kazilas – 2019 – diva-portal.org
Page 1. Author: Panagiotis Kazilas Supervisor: Sabri Pllana Examiner: Narges Khakpour Reader: Narges Khakpour Semester: VT 2018 Course Code: 4DV50E Subject: Computer Science Master Thesis Project Augmenting MPI Programming Process with Cognitive Computing …

Multimodal dialogue processing for machine translation
A Waibel – The Handbook of Multimodal-Multisensor Interfaces …, 2019 – dl.acm.org
Page 1. 14Multimodal Dialogue Processing for Machine Translation Alexander Waibel 14.1 Introduction Humans converse with each other to communicate and to develop ideas interac- tively in the presence of imprecise and under-specified information …

Exploring the value of the Bregman Block Average Co-clustering algorithm for missing value imputation in geo-referenced time series
JM Timmermans – 2019 – dspace.library.uu.nl
… Image analysis: face detection on a mobile phone, and automatic building recognition from satelite im- agery. • Text analysis: filtering spam emails and customer support chat-bots. • Data mining, finding disease patterns in medical data …

Early integration for movement modeling in latent spaces
R Hornung, N Chen, P van der Smagt – The Handbook of Multimodal …, 2019 – dl.acm.org
Page 1. 8Early Integration for Movement Modeling in Latent Spaces Rachel Hornung, Nutan Chen, Patrick van der Smagt 8.1 Introduction In this chapter, we will show how techniques of advanced machine and deep learn- ing …

Standardized representations and markup languages for multimodal interaction
R Tumuluri, D Dahl, F Paternò… – The Handbook of …, 2019 – dl.acm.org
Page 1. 9Standardized Representations and Markup Languages for Multimodal Interaction Raj Tumuluri, Deborah Dahl, Fabio Patern`o, Massimo Zancanaro 9.1 Introduction This chapter discusses some standard languages …

Deep Learning Language Modeling Workloads: Where Time Goes on Graphics Processors
AH Zadeh, Z Poulos, A Moshovos – 2019 IEEE International …, 2019 – ieeexplore.ieee.org
Page 1. Deep Learning Language Modeling Workloads: Where Time Goes on Graphics Processors Ali Hadi Zadeh, Zissis Poulos, Andreas Moshovos Department of Electrical & Computer Engineering, University of Toronto {hadizade,zpoulos,moshovos}@ece.utoronto.ca …

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 …

Game-Theoretic Safety Assurance for Human-Centered Robotic Systems
JF Fisac – 2019 – escholarship.org
… has largely prevented the application of learning-based methods to safety-critical or high-stakes systems, while a number of contexts where they have been applied have seen serious issues related to unexpected and poorly understood behavior (from chat bots using offensive …

Game-Theoretic Safety Assurance for Human-Centered Robotic Systems
J Fernandez Fisac – 2019 – escholarship.org
… has largely prevented the application of learning-based methods to safety-critical or high-stakes systems, while a number of contexts where they have been applied have seen serious issues related to unexpected and poorly understood behavior (from chat bots using offensive …

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 …

A Systematic Approach for Automatically Answering General-Purpose Objective and Subjective Questions
LP Acharya – 2019 – repository.lib.fit.edu
… 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 … This system utilizes the maximum entropy model for questions and answers classification. The …

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 …

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 …

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 …