Decision Tree & Dialog Systems

Notes:

A decision tree is a graphical representation of a decision-making process that shows the various options or choices available to a decision maker, and the possible outcomes or consequences of each choice. Decision trees are often used to model decision-making processes in business, economics, and other fields, as they provide a clear and visual way to understand and analyze complex decision-making situations.

In a decision tree, the root node represents the initial decision or choice, and the branches represent the different options or alternatives. The leaf nodes represent the final outcomes or consequences of the decision, and the path from the root node to a leaf node represents the series of decisions and choices made along the way.

Decision trees can be used to evaluate the potential risks and rewards of different choices, and to identify the optimal course of action based on the available information. They are often used in conjunction with other decision-making tools, such as decision tables and decision matrices, to help decision makers make informed and effective decisions.

Decision trees can be used in dialog systems to model and manage the flow of conversation and to generate appropriate responses to user input.

In a dialog system, the root node of the decision tree may represent the initial state of the conversation, and the branches may represent different options or paths that the conversation can take based on the user’s input. For example, if the conversation is about booking a hotel room, the root node may represent the initial request for information, and the branches may represent different options for the type of room, location, and other preferences.

As the user provides input, the system can use the decision tree to determine the appropriate response based on the current state of the conversation and the user’s preferences. For example, if the user indicates that they want a room with a view, the system can follow the appropriate branch of the decision tree to generate a response that presents the available options for rooms with a view.

Decision trees can be a useful tool for managing the flow of conversation and generating appropriate responses in a dialog system. They can help the system understand and respond to user input in a logical and structured way, and they can be used to evaluate the potential risks and rewards of different conversation paths to help the system choose the most appropriate response.

Adaboost is a machine learning algorithm that can be used to improve the performance of decision trees by combining multiple weak decision trees into a single strong model. Adaboost works by iteratively building and training a series of decision trees, and then combining them in a way that minimizes the overall error rate.
Annotated decision tree is a decision tree that has been labeled or annotated with additional information or metadata to aid in the interpretation and analysis of the tree. Annotated decision trees may include labels or descriptions for each node and each branch, as well as other information such as probabilities or costs associated with each decision or outcome.
Applied decision tree is a decision tree that has been developed and used to solve a specific problem or make a specific decision. Applied decision trees are often used in business and other practical contexts to evaluate the potential risks and rewards of different choices, and to identify the optimal course of action based on the available information.
Binary decision tree is a decision tree in which each non-leaf node has exactly two branches, representing two possible choices or options. Binary decision trees are often used in classification tasks, where the goal is to predict the class or category of an input based on a set of features or attributes. Binary decision trees are a simple and efficient way to model decision-making processes and classify data, and they are widely used in a variety of applications.
Boosted decision tree is a type of decision tree that has been improved or “boosted” using a machine learning algorithm such as Adaboost. Boosted decision trees are often used in classification tasks, and they are designed to be more accurate and robust than traditional decision trees by combining the predictions of multiple weak decision trees into a single strong model.
Branching decision tree is a decision tree in which the root node is split into multiple branches, representing different options or choices. Branching decision trees are used to model and analyze decision-making processes, and they can be used to evaluate the potential risks and rewards of different choices and identify the optimal course of action.
Complex decision tree is a decision tree that has a large number of nodes and branches, and that involves a large number of decisions and options. Complex decision trees may be more difficult to interpret and analyze than simpler decision trees, and they may require more data and resources to build and evaluate.
Complicated decision tree is a decision tree that is difficult to understand or analyze due to its complexity or the amount of information it contains. Complicated decision trees may be more difficult to interpret and use than simpler decision trees, and they may require more time and resources to build and evaluate.
Contextual decision tree is a decision tree that takes into account the specific context or situation in which the decision is being made. Contextual decision trees may consider factors such as the goals and objectives of the decision maker, the available resources and constraints, and the potential risks and rewards of different choices.
Data-driven decision tree is a decision tree that is built and evaluated using data and statistical analysis. Data-driven decision trees are often used in predictive modeling and data classification tasks, and they are designed to identify patterns and trends in the data that can be used to make accurate and reliable predictions.
Decision analysis tool is a tool or method that is used to assist in the process of making decisions. Decision analysis tools may include decision trees, decision tables, decision matrices, and other techniques that help decision makers evaluate the potential risks and rewards of different choices and identify the optimal course of action.
Decision tree algorithm is a computer program or set of steps that is used to construct a decision tree. Decision tree algorithms typically involve identifying the relevant features or attributes of the data, evaluating the potential splits or divisions of the data based on these features, and constructing the tree based on the optimal splits. Decision tree algorithms are widely used in machine learning and data analysis, and they are often used in conjunction with other techniques such as boosting or pruning to improve the performance of the tree.
Decision tree classifier is a machine learning model that uses a decision tree to classify data into different categories or classes. Decision tree classifiers are often used in classification tasks, such as predicting the type of an email (spam or not spam) based on the contents of the email, or predicting the species of an animal based on its physical characteristics.
Decision tree clustering is a method of clustering data that uses a decision tree to group similar data points together. Decision tree clustering involves evaluating the similarity of the data based on a set of features or attributes, and constructing a tree to represent the relationships between the data points.
Decision tree flow refers to the sequence of decisions and choices represented by a decision tree. In a decision tree, the flow refers to the path from the root node to a leaf node, and it represents the series of decisions and choices made along the way.
Decision tree learner is a machine learning algorithm that is used to build and train a decision tree model. Decision tree learners typically involve identifying the relevant features or attributes of the data, evaluating the potential splits or divisions of the data based on these features, and constructing the tree based on the optimal splits. Decision tree learners are often used in conjunction with other techniques such as boosting or pruning to improve the performance of the tree.
Decision tree learning is a machine learning technique that involves building and training a decision tree model to predict or classify data based on a set of features or attributes. Decision tree learning algorithms are widely used in a variety of applications, including predictive modeling, data classification, and decision-making.
Decision tree learning algorithm is a computer program or set of steps that is used to construct and train a decision tree model. Decision tree learning algorithms typically involve identifying the relevant features or attributes of the data, evaluating the potential splits or divisions of the data based on these features, and constructing the tree based on the optimal splits. Decision tree learning algorithms are often used in conjunction with other techniques such as boosting or pruning to improve the performance of the tree.
Decision tree model is a machine learning model that represents a decision-making process as a tree-like graph or model. Decision tree models are used to predict or classify data based on a set of features or attributes, and they are widely used in a variety of applications, including predictive modeling, data classification, and decision-making.
Decision tree-based estimation is a method of estimating or predicting a value or outcome using a decision tree model. Decision tree-based estimation involves evaluating the relevant features or attributes of the data, and using the decision tree model to identify the most likely value or outcome based on these features. Decision tree-based estimation is often used in conjunction with other techniques, such as regression analysis, to improve the accuracy of the estimate.
Dialog decision tree is a decision tree that is used to model and manage the flow of conversation in a dialog system. Dialog decision trees are often used to generate appropriate responses to user input and to guide the conversation in a specific direction based on the current context and the user’s preferences.
Enhanced decision tree is a decision tree that has been improved or “enhanced” using a machine learning algorithm or other technique. Enhanced decision trees may be more accurate or efficient than traditional decision trees, and they may be able to handle more complex or varied data.
Factorized decision tree is a decision tree in which the data has been “factorized” or transformed into a different representation that is more suitable for the decision tree model. Factorized decision trees may be more accurate or efficient than traditional decision trees, and they may be able to handle more complex or varied data.
Fixed decision tree is a decision tree that has been pre-determined or “fixed” based on a specific set of data or decision-making criteria. Fixed decision trees may be used in situations where the data or decision-making criteria are known in advance and do not change over time. Fixed decision trees are typically simpler and more efficient than decision trees that are built and trained on the fly, but they may be less flexible and adaptable to changing data or circumstances.
Fuzzy decision tree is a decision tree that uses fuzzy logic to model and analyze decision-making processes. Fuzzy logic is a type of logic that allows for the representation and manipulation of uncertain or imprecise data, and it is often used in situations where the data is noisy or incomplete. Fuzzy decision trees may be more robust and flexible than traditional decision trees, as they can handle a greater range of data and uncertainties.
Handcrafted decision tree is a decision tree that has been manually created or “handcrafted” by a human expert, rather than being generated automatically by a machine learning algorithm. Handcrafted decision trees may be more accurate and reliable than automated decision trees, as they are based on the knowledge and expertise of the human expert. However, they may be more time-consuming and resource-intensive to create and maintain.
Hierarchical binary decision tree is a decision tree in which each non-leaf node has exactly two branches, representing two possible choices or options, and the tree is organized in a hierarchical or nested structure. Hierarchical binary decision trees are often used in classification tasks, and they are designed to be efficient and easy to interpret.
KALDI toolkit is a software toolkit for speech processing that includes a decision tree-based speech recognition system. The KALDI toolkit decision tree is a decision tree that is used in the KALDI toolkit to model and recognize speech patterns and sounds. The KALDI toolkit decision tree is designed to be efficient and accurate, and it is widely used in speech processing and recognition applications.
Learned decision tree is a decision tree that has been built and trained using machine learning algorithms. Learned decision trees are typically based on a large dataset and are designed to identify patterns and trends in the data that can be used to make accurate predictions or classifications.
Meta decision tree is a decision tree that is used to analyze or evaluate other decision trees. Meta decision trees may be used to compare the performance of different decision trees, to identify common patterns or characteristics of decision trees, or to evaluate the overall effectiveness of decision tree-based approaches.
OCC decision tree is a decision tree that is used for online classification and clustering tasks. OCC decision trees are designed to handle data that is arriving in a stream or sequence, and they are often used in real-time or dynamic applications where the data is constantly changing.
Optimal decision tree is a decision tree that is considered to be the best or most effective solution for a specific problem or task. Optimal decision trees may be based on specific criteria or measures, such as accuracy, efficiency, or simplicity, and they may be identified using optimization algorithms or other techniques.
Part-of-speech tagging is the process of identifying the part of speech (such as noun, verb, adjective, etc.) of each word in a text. Part-of-speech tagging using decision tree involves building and training a decision tree model to classify the part of speech of each word based on its context and other features.
Phonetic decision tree is a decision tree that is used to model and analyze phonetic sounds or patterns. Phonetic decision trees may be used in speech processing and recognition applications, and they may be based on features such as the frequency, duration, and intensity of different sounds.
Pictorial decision tree is a decision tree that is represented using visual elements such as icons, images, or diagrams, rather than text or numbers. Pictorial decision trees may be easier to understand and interpret than traditional decision trees, and they may be more effective at communicating complex or abstract concepts.
Probabilistic decision tree is a decision tree that takes into account the probabilities or likelihoods of different outcomes or events. Probabilistic decision trees may be used to evaluate the risks and rewards of different choices or actions, and they may be based on statistical analysis or other techniques to estimate the probabilities of different outcomes.
Prosodic decision tree is a decision tree that is used to model and analyze prosodic features of speech, such as pitch, duration, and stress. Prosodic decision trees may be used in speech processing and recognition applications, and they may be based on features such as the pitch contour, duration patterns, and stress patterns of different sounds.
Trained decision tree models are decision tree models that have been built and trained using machine learning algorithms. Trained decision tree models are typically based on a large dataset, and they are designed to identify patterns and trends in the data that can be used to make accurate predictions or classifications. Trained decision tree models are often used in conjunction with other machine learning techniques, such as boosting or pruning, to improve their performance.

Wikipedia:

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What Should I Ask? Using Conversationally Informative Rewards for Goal-Oriented Visual Dialog
P Shukla, C Elmadjian, R Sharan, V Kulkarni… – arXiv preprint arXiv …, 2019 – arxiv.org
… We propose an end-to-end goal-oriented visual dialogue system, that combines rein- forcement learning with regularized informa- tion gain … Building natural language models that are able to converse towards a specific goal is an active area of research that has attracted a lot of …

End-to-end Gated Self-attentive Memory Network for Dialog Response Selection
S Sun, YC Tam, J Cao, C Yan, Z Fu, C Niu, J Zhou – 2019 – cs.utah.edu
… LightGBM: A highly efficient gradient boosting decision tree … In Empirical Meth- ods in Natural Language Processing (EMNLP), 1532–1543 … Building end-to-end dialogue systems us- ing generative hierarchical neural network models. In AAAI, volume 16, 3776–3784 …

Multi-Agent Actor-Critic Reinforcement Learning for Argumentative Dialogue Systems
Y Yang – 2019 – academia.edu
… Enabling computer systems to converse with human in natural language has always been one of the most attractive and complicated fields of artificial intelligence. The last decade has witnessed many impressive breakthroughs in Spoken Dialogue System (SDS), a sys- tem …

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
… Most artificial intelligence amplifies and automates this pattern … 26 Mashael Al-Luhaybi, Leila Yousefi, Stephen Swift, Steve Counsell, and Allan Tucker The Impact of Student Model Updates on Contingent Scaffolding in a Natural-Language Tutoring System …

A hierarchical decoding model for spoken language understanding from unaligned data
Z Zhao, S Zhu, K Yu – ICASSP 2019-2019 IEEE International …, 2019 – ieeexplore.ieee.org
… spoken language understanding (SLU) module is a key component of spoken dialogue system (SDS), parsing … before, and [20] is a statistical method which uses decision trees based binary … of the 2015 Conference on Empirical Meth- ods in Natural Language Processing, 2015 …

Joint dialog act segmentation and recognition in human conversations using attention to dialog context
T Zhao, T Kawahara – Computer Speech & Language, 2019 – Elsevier
… In a quest to achieve more natural and intelligent dialog systems, we have been building conversational robots with whom users can … Towards automatic segmentation, they explored a decision tree (DT) featuring pause information, a hidden-event language model (HE-LM), and …

Text Classification With Deep Neural Networks
T Huynh – 2019 – oro.open.ac.uk
… learned representations to be used with the discussed Deep Neural Net- works in different NLP tasks such as Dialog Systems, Machine Translation or Natural Language Inference. Page 4 … one of the central problems of Artificial Intelligence. The field of Natural Language …

Comfort or safety? Gathering and using the concerns of a participant for better persuasion
E Hadoux, A Hunter – Argument & Computation, 2019 – content.iospress.com
… make. For instance, the persuadee might be restricted to only making arguments by selecting them from a menu in order to obviate the need for natural language processing of arguments being entered (as illustrated in Fig. 1 …

A multilingual and multidomain study on dialog act recognition using character-level tokenization
E Ribeiro, R Ribeiro, DM de Matos – Information, 2019 – mdpi.com
… However, recently, similar to many other Natural Language Processing (NLP) tasks [18,19], most studies on … it considers information from future segments, which is not available to a dialog system … labels, which can be attributed to segments using a decision tree, stopping when …

A mixed methods analysis of the adoption and diffusion of chatbot technology in the German insurance sector
D Rodríguez Cardona, O Werth, S Schönborn… – 2019 – aisel.aisnet.org
… Chatbot can be defined as a conversational software interface or a computer-based dialog system in which … of sophistication and design, a conversational interaction can be built on a decision-tree logic or can be activated through sophisticated natural language (NL) queries …

Sarcasm identification in textual data: systematic review, research challenges and open directions
CI Eke, AA Norman, L Shuib, HF Nweke – Artificial Intelligence Review, 2019 – Springer
… Keywords Sarcasm identification · Social media data · Natural language processing · Pre- processing · Feature … Moreover, sarcasm identifica- tion is useful in dialogue, system review ranking, and … of Naïve Bayes (NB), support vector machine (SVM), decision tree (DT), random …

Emotion-Aware and Human-Like Autonomous Agents
N Asghar – 2019 – uwspace.uwaterloo.ca
… In 31st Conference on Uncertainty in Artificial Intelligence (UAI), 2015. [10] … A prime example is a dialogue system, where the agent should converse fluently and coher- ently with a user and connect with them emotionally … 11 2.4 Deep Learning for Natural Language Processing …

Analyzing Coreference Tools for NLP Application
S Singh, K Bhattacharjee, H Darbari, S Verma – 2019 – researchgate.net
… of integration for building an NLP application like summarization system, dialogue system etc … approaches statistical methods like naive-bayes based model, decision-tree based approach … His research interests includes the area of Natural Language Processing (NLP), Machine …

Contributions to Social Learning Analytics based on Sentiment Analysis of Students’ Interactions in Educational Environments
MJD Torres – 2019 – catarina.udlap.mx
… machine learning algorithms were used, such as SVM, Naïve Bayes, logistic regression and a decision tree … Among the many areas of application of AI and ML, we find Natural Language … of computer dialogue systems (Chen, Liu, Yin & Tang, 2017), machine translation (Gaspari …

Human-inspired socially-aware interfaces
D Schiller, K Weitz, K Janowski, E André – International Conference on …, 2019 – Springer
… Various attempts have been made to implement empathic behaviors in computer-based dialogue systems … model for empathic behaviors was created using methods from machine learning (Naïve Bayes and decision trees) … In: Wilks, Y. (ed.) Natural Language Processing, vol …

Offline and online satisfaction prediction in open-domain conversational systems
JI Choi, A Ahmadvand, E Agichtein – Proceedings of the 28th ACM …, 2019 – dl.acm.org
… AI [26] has been an active area of research for decades, and based on recent advances in natural language un- derstanding … Dialogue system technology challenges (DSTC), originally known as the dialogue state tracking challenges, were initiated in 2013 in order to promote …

Speech technology progress based on new machine learning paradigm
V Deli?, Z Peri?, M Se?ujski, N Jakovljevi?… – Computational …, 2019 – hindawi.com
… Message Meaning Artificial Intelligence Natural Language Processing Speech Technologies (multimodal communication) Text Text SLG SLU TTS ASR = = = = Figure 4: Components of a human-machine speech dialogue system. Computational Intelligence and Neuroscience …

Network Diffusion and Technology Acceptance of A Nurse Chatbot for Chronic Disease Self-Management Support: A Theoretical Perspective
JPT Hernandez – The Journal of Medical Investigation, 2019 – jstage.jst.go.jp
… CDSMS decision trees provide codifiable rules for chatbot interventions, eg, the quasi-experimental study by … second (29) indicating poor natural language processing/NLP and intelligent response … pdf 39!Zhao T. Learning generative end-to-end dialog systems with …

Multiple Generative Models Ensemble for Knowledge-Driven Proactive Human-Computer Dialogue Agent
Z Dai, W Liu, G Zhan – arXiv preprint arXiv:1907.03590, 2019 – arxiv.org
… been recognized as one of the most attractive goals in natural language process (NLP … We employed a Gradient Boosting Decision Tree (GBDT) regressor (Friedman, 2001) to score the all … reference for building an end-to-end highly- performed human-computer dialogue system …

Spoken Language Understanding of Human-Machine Conversations for Language Learning Applications
Y Qian, R Ubale, P Lange, K Evanini… – Journal of Signal …, 2019 – Springer
… ASR), which decodes the input speech into text, and the natural language understanding (NLU … Crowdsourced non-native speakers of English interacted with the spoken dialog system in the job … DNN consists of the senones of the HMM obtained by decision-tree based clustering …

Designing for health chatbots
A Fadhil, G Schiavo – arXiv preprint arXiv:1902.09022, 2019 – arxiv.org
… irregular cases, such as a keyword related to another branch of the decision tree or a … avatar used to build natural dialogue with users, as in health dialogue systems with conversational … Adding graphical widgets with natural language could enhance user interaction with the bot …

Semantically aware text categorisation for metadata annotation
G Carducci, M Leontino, DP Radicioni… – … Conference on Digital …, 2019 – Springer
… or concepts, in contrast with abstracts that are more exhaustive and written in fully fledged natural language … Namely, 50 decision trees were trained, each of them assigning either a class label 0 or 1 to a … In: Proceedings of the 21st National Conference on Artificial Intelligence …

A Human-in-the-Loop Method for Developing Machine Learning Applications
L Yang, M Li, J Ren, C Zuo, J Ma… – 2019 6th International …, 2019 – ieeexplore.ieee.org
… gbt n estimators: number of the decision tree randint(2, 35)*10 … as entity identification and entity extrac- tion, is a subtask of Natural Language Processing (NLP … for many NLP tasks, relation extraction, event extraction, knowledge graph, machine translation and dialogue system …

Conception and implementation of a vocal assistant for the use in vehicle diagnostics
M Yacoub – 2019 – elib.uni-stuttgart.de
… 6 Implementation 32 6.1 Decision Trees … Let us in the following focus on the basics of dialogue systems and their architecture (see Figure 3). It consists … Furthermore it performs speech to text oper- ations (and vice versa), Natural language processing (NLP, see section 2.1) …

An automatic short-answer grading model for semi-open-ended questions
L Zhang, Y Huang, X Yang, S Yu… – Interactive Learning …, 2019 – Taylor & Francis
… X. (2014). Autotutor and family: A review of 17 years of natural language tutoring. International Journal of Artificial Intelligence in Education, 24(4), 427–469. doi: 10.1007/s40593-014-0029-5[Crossref] , [Google Scholar]). The …

Identification of Sarcasm in Textual Data: A Comparative Study
P Mehndiratta, D Soni – Journal of Data and Information …, 2019 – content.sciendo.com
Jump to Content Jump to Main Navigation …

Comparison and efficacy of synergistic intelligent tutoring systems with human physiological response
F Alqahtani, N Ramzan – Sensors, 2019 – mdpi.com
… They can be divided into broad categories such as Bayesian Classification, Decision Tree method, Fuzzy Classifiers, Neural Networks, and Genetic Algorithm Classification, etc. [75]. Each of these categories in turn consists of multiple specific types of algorithms …

A narrative sentence planner and structurer for domain independent, parameterizable storytelling
SM Lukin, MA Walker – Dialogue & Discourse, 2019 – 129.70.43.92
… Marilyn A. Walker MAWALKER@UCSC.EDU Natural Language and Dialogue Systems Lab University of California, Santa Cruz, CA Editor: Vera Demberg Submitted 07/2017; Accepted 04/2019; Published online 05/2019 Abstract …

Neural Language Models with Explicit Coreference Decision
J Kunz – 2019 – diva-portal.org
… January 2, 2019 Supervisors: Christian Hardmeier, Uppsala University Page 2. Abstract Coreference is an important and frequent concept in any form of discourse, and Coreference Resolution (CR) a widely used task in Natural Language Understanding (NLU) …

Multimodal sentiment analysis: A survey and comparison
R Kaur, S Kautish – International Journal of Service Science …, 2019 – igi-global.com
… recognitionsystemrealistictotheHRIhasbeendesignated.Theirgrindhas beenrealisticinageneral interface(ordialog)system,calledRDS … classificationmethodstocategorizetext(Medhat etal.,2014)Manyinstancesincludeinthisbrandofmethodincludedecisiontreeclassifier,linear …

It’s How You Say It: Identifying Appropriate Register for Chatbot Language Design
AP Chaves, E Doerry, J Egbert, M Gerosa – Proceedings of the 7th …, 2019 – dl.acm.org
… In chatbots, natural language conversation is the primary re- source for achieving interactional goals … For example, most Facebook Messenger chatbots, such as UPS and Sephora, use a simple decision tree mechanism combined with visual elements to produce pre-defined …

Emotion Analysis for Opinion Mining From Text: A Comparative Study
AM Mohsen, AM Idrees, HA Hassan – International Journal of e …, 2019 – igi-global.com
… technique (Turney & Litiman, 2003) is based on natural language processingparadigm … EPcorpus Unknown Dialogue systems happiness,pleasantness,relief, fear,sadness,disappointment, unpleasantness … MachinelearningisanArtificialIntelligence(AI)paradigmthataimsat …

A review of deep learning based speech synthesis
Y Ning, S He, Z Wu, C Xing, LJ Zhang – Applied Sciences, 2019 – mdpi.com
… scenarios such as intelligent speech interaction, chatbot or conversational artificial intelligence (AI … internal structures in the real world (eg, speech, natural language, image, video … maps linguistic features into probability densities of speech parameters with various decision trees …

Emoji Identification And Prediction In Hebrew Political Corpus
C Liebeskind – Issues in Informing Science & Information …, 2019 – search.ebscohost.com
… the short message text could contribute to the improvement of various Natural Language Processing (NLP … and tf-idf weights on the task of emoji recommendation in multi- turn dialogue systems … selected out of eight classifiers, ie, Bernulli Naive Bayes, Decision Tree, Lo- gistic …

Modeling Behavior Patterns with an Unfamiliar Voice User Interface
CM Myers, D Grethlein, A Furqan, S Ontañón… – Proceedings of the 27th …, 2019 – dl.acm.org
… al. con- struct user models using decision tree learning algorithm to cus- tomize dialogue … per utterance Unknowns The number of failures DiscoverCal’s Natural Language Processor experienced … Predicting and adapting to poor speech recognition in a spoken dialogue system …

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
… module, a dialog state tracker, a dialog policy learning module, and a natural language generation module … Session: Long – Question Answering and Dialogue Systems I … seq2seq model to generate a response and then adopts a Gradient Boosting Decision Tree (GBDT) ranker …

Deep Weighted Feature Descriptors for Lip Reading of Kannada Language
MS Nandini, TC Nagavi… – 2019 6th International …, 2019 – ieeexplore.ieee.org
… I. INTRODUCTION Natural Language Processing is a stream with various problems to be … The Decision tree classification algorithm has provided a good result over a benchmark dataset … SM Omohundro, and J. Shi, “Towards a Robust Speechreading Dialog System,” NATO ASI …

Multimodal integration for interactive conversational systems
M Johnston – The Handbook of Multimodal-Multisensor Interfaces …, 2019 – dl.acm.org
… In weighted transducers the best path is the path with the lowest overall cost. Frames are a data structure that have been used in a variety of artificial intelligence applications including natural language processing, computer vision, and knowledge representation and reasoning …

Topic-enriched word embeddings for sarcasm identification
A Onan – Computer Science On-line Conference, 2019 – Springer
… algorithms, such as, support vector machines, logistic regression, Naïve Bayes and decision trees … In: Proceedings of the 6th Dialog System Technology Challenges Workshop (2017)Google … of the 2014 Conference on Empirical Methods in Natural Language Processing, pp …

A survey on construction and enhancement methods in service chatbots design
Z Peng, X Ma – CCF Transactions on Pervasive Computing and …, 2019 – Springer
… 2, modular task-oriented dialog system mainly consists of three components (Shum et al … of works have sought to use DNN-based methods to handle request-response matching in natural language processing (Lu … 7b). They deployed a Gradient Boosting Decision Tree (Ye et al …

Survey of Temporal Information Extraction.
CG Lim, YS Jeong, HJ Choi – JIPS, 2019 – jips-k.org
… field. The Informatics for Integrating Biology and the Bedside (i2b2) offered a natural language processing (NLP) challenge in 2012 [20] … expressions. The temporal relations between events were extracted using decision trees (DT) …

Emoji prediction for Hebrew political domain
C Liebeskind, S Liebeskind – … Proceedings of The 2019 World Wide Web …, 2019 – dl.acm.org
… the short text of a message may help to improve different Natural Language Pro- cessing … [47] investigated the task of emoji recommendation in multi-turn dialogue systems … sification framework using eight ML methods: Bernulli Naive Bayes (NB), Decision Tree (DT), Logistic …

Enhancing text using emotion detected from EEG signals
A Gupta, H Sahu, N Nanecha, P Kumar, PP Roy… – Journal of Grid …, 2019 – Springer
… wise, the authors in [36] have used language modeling techniques to characterize the syntactic and struc- tural complexity of web search queries with natural language … The classifier is an ensemble of decision trees where the vote of the trees decides the final class for a sample …

Emotion recognition from speech: An implementation in MATLAB
MAA Wusu-Ansah – 2019 – air.ashesi.edu.gh
… Knowledge about the emotional state can help to connect angry callers of an automatic dialogue system to a human … of syllables and larger units of speech) parameters of a natural language. Acoustic … Prediction Speed Memory Usage Decision Trees Yes Fast Small Discriminant …

The next generation: chatbots in clinical psychology and psychotherapy to foster mental health–a scoping review
E Bendig, B Erb, L Schulze-Thuesing… – Verhaltenstherapie, 2019 – karger.com
… that use nat- ural-language, speech-based interfaces in dialogue systems (such as … natural language processing (NLP), which is the machine processing of natural language using statistical … The chatbot internally follows a predefined decision tree [Chowdhury, 2003; Smola et al …

PathBot: An Intelligent Chatbot for Guiding Visitors and Locating Venues
K Mabunda, A Ade-Ibijola – 2019 6th International Conference …, 2019 – ieeexplore.ieee.org
… 1) Rule-based Chatbots: Rule-based Chatbots also called decision-tree bots are Chatbots that use the if/then rules statements [37] … Example-based dialog modeling for practical multi-domain dialog system … [21] Elizabeth D Liddy. Natural language processing. 2001 …

MSc in Computer Science
RB Sulaiman – researchgate.net
… system for the company. This system will work based on the text as a conversational agent that can interact with humans by natural language … The primary concern of this project includes natural language processing (NLP), machine learning and the vector space model (VSM) …

Dive into deep learning
A Zhang, ZC Lipton, M Li, AJ Smola – Unpublished Draft. Retrieved, 2019 – academia.edu
Page 1. Dive into Deep Learning Aston Zhang Zachary C. Lipton Mu Li Alexander J. Smola Mar 13, 2019 Page 2. Page 3. This draft is a testing version (draft date: March 13, 2019). Visit https://d2l.ai to obtain a later or release version. i Page 4. ii Page 5. Contents Preface 1 …

Empathic Response Generation in Chatbots.
T Spring, J Casas, K Daher, E Mugellini… – SwissText, 2019 – ceur-ws.org
… such as the Support Vector Machine (SVM) (Teng et al., 2006), Naive Bayes, or Decision Trees … In Natural Language Process- ing and Information Systems, Springer Berlin Hei- delberg, pages 27–39 … A retrieval- based dialogue system utilizing utterance and context embeddings …

Opinion Analysis in Interactions: From Data Mining to Human-Agent Interaction
C Clavel – 2019 – books.google.com
… We shall present methods based on artificial intelligence (through learning models of socio-emotional behaviors, combining symbolic and machine learning … Later, I extended my field of study from acoustic analysis to natural language processing in the context of opinion …

Early detection of user engagement breakdown in spontaneous human-humanoid interaction
AB Youssef, C Clavel, S Essid – IEEE Transactions on Affective …, 2019 – ieeexplore.ieee.org
… turn-taking model [35]. The communication breakdown in spoken dialogue systems was studied in [39] … By comparing logistic regression and boosted decision tree models in [37], the logistic regression model was selected for managing dis- engagement decisions …

Machine learning for the recognition of emotion in the speech of couples in psychotherapy using the Stanford Suppes Brain Lab Psychotherapy Dataset
CE Crangle, R Wang, M Perreau-Guimaraes… – arXiv preprint arXiv …, 2019 – arxiv.org
… Using data collected from AT&T’s natural-language human-computer spoken dialog system, “How May … We chose the method of random forests, which is derived from decision trees and uses the random selection of feature variables to construct a collection of decision …

DATE OF PUBLICATION July 2019
T Walsh, N Levy, G Bell, A Elliott, J Maclaurin… – 2019 – researchgate.net
… 93 3.5.1 Spoken and text-based dialogue systems 93 … Artificial intelligence encompasses a number of methods, including machine learning (ML), natural language processing (NLP), speech recognition, computer vision and automated reasoning …

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) … To emulate this type of learning, behavioral learning has been applied to artificial intelligence agents. This disci … LfI allows the user to issue instructions in natural language …

ASRoIL: a comprehensive survey for automatic speech recognition of Indian languages
A Singh, V Kadyan, M Kumar, N Bassan – Artificial Intelligence Review, 2019 – Springer
Page 1. Vol.:(0123456789) Artificial Intelligence Review https://doi.org/10.1007/s10462-019- 09775-8 1 3 ASRoIL: a comprehensive survey for automatic speech recognition of Indian languages Amitoj Singh1 · Virender Kadyan2 · Munish Kumar1 · Nancy Bassan3 …

Dynamic Search–Optimizing the Game of Information Seeking
Z Tang, GH Yang – arXiv preprint arXiv:1909.12425, 2019 – arxiv.org
… The advancement of information technology and artificial intelligence have made access to large amounts of … Figure 8 shows a common pipeline in dialogue systems. In this pipeline, first, human utterances go through a natural language understanding (NLU) component to get …

Semantic Role Labeling for Indian languages
A Gupta – 2019 – web2py.iiit.ac.in
… in various NLP applications such as semantic role labeling, machine translation, dialogue systems and discourse … Machine transla- tion (Automatic translation of one human language to another), Natural language generation (Generate … [62] used a C5 decision tree classifier and …

Machine reading comprehension: a literature review
X Zhang, A Yang, S Li, Y Wang – arXiv preprint arXiv:1907.01686, 2019 – arxiv.org
… Received: date / Accepted: date Abstract Machine reading comprehension aims to teach machines to understand a text like a human, and is a new challenging direction in Artificial Intelligence … Keywords Machine Reading Comprehension · Natural Language Processing · More …

Effectiveness of data-driven induction of semantic spaces and traditional classifiers for sarcasm detection
MA Di Gangi, GL Bosco, G Pilato – arXiv preprint arXiv:1904.04019, 2019 – arxiv.org
… modeling, speech recognition, smart indexing, anti-spam filters, dialogue systems, and other … modeling, speech recognition, smart indexing, and other statistical natural language process- ing … Decision trees (Kotsiantis 2013) are rooted trees that can be used successfully as …

Positive Unlabelled Learning to Recognize Dishes as Named Entity
A TAREK – 2019 – bspace.buid.ac.ae
… their smart digital assistant, which resulted in Amazon comprehend (Amazon, 2018). Google has Natural Language API which has entity recognition capabilities among other text analysis tools … a system, named Slugbot’s Named Entity Recognition for dialogue Systems …

Business Sentiment Analysis. Concept and Method for Perceived Anticipated Effort Identification
N Rizun, A Revina – Concept and Method for Perceived Anticipated …, 2019 – papers.ssrn.com
… ML algorithms are Linear Classifiers (Support Vector Machine, Neural Network), Decision Trees, Rule-based … image, track customer feedbacks and in development of automatic dialogue systems [30], [27 … of the 29th International Conference on Tools with Artificial Intelligence, pp …

Sémantické porozum?ní konverzaci
P Lorenc – 2019 – dspace.cvut.cz
… Combination of both — At the beginning of the statistical approach was very popular to use Decision trees which combines both approaches … 3 Page 22. 1. Natural Language Processing Figure 1.1: Predicted fields of chatbot usage[1] 1.1 Chatbots …

Multimodal Integration
M Johnston – The Handbook of Multimodal-Multisensor Interfaces …, 2019 – books.google.com
… In weighted transducers the best path is the path with the lowest overall cost. Frames are a data structure that have been used in a variety of artificial intelligence applications including natural language processing, computervision, and knowledge representation and reasoning …

Empirical study and improvement on deep transfer learning for human activity recognition
R Ding, X Li, L Nie, J Li, X Si, D Chu, G Liu, D Zhan – Sensors, 2019 – mdpi.com
… Some researchers studied the switching of conversation models in different scenarios within NLP (Natural Language Processing), so that the dialogue system can satisfy the needs of users [15]. We focus on the transfer learning based on features … [6] used decision tree with k …

The evolution of argumentation mining: From models to social media and emerging tools
A Lytos, T Lagkas, P Sarigiannidis… – Information Processing & …, 2019 – Elsevier
… Toulmin, 2003), which gained the interest of the scientific community because of its potential when novel Artificial Intelligence (AI) algorithms … The medium for arguing for human beings is natural language, whereas the input for ML algorithms and techniques should be distinct …

Tweet Act Classification: A Deep Learning based Classifier for Recognizing Speech Acts in Twitter
T Saha, S Saha, P Bhattacharyya – 2019 International Joint …, 2019 – ieeexplore.ieee.org
… which includes considerable works such as [5], wherein the authors employed a range of techniques such as Decision Trees, Hidden Markov … also shown success in various NLP tasks such as automatic speech recognition [19], sentiment analysis [20], dialogue systems [21] and …

Annotation-efficient approaches towards real-time emotion recognition
IP Lajos – 2019 – ritsumei.repo.nii.ac.jp
… The author argues that affective state-interdependent intentions can improve emotion/sentiment/ polarity classification even on small sets of labeled data, thus being applicable for the pre-training of commercial games, dialogue systems, and other applications … Decision trees …

A deep look into neural ranking models for information retrieval
J Guo, Y Fan, L Pang, L Yang, Q Ai, H Zamani… – Information Processing …, 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 …

Computing Happiness from Textual Data
E Mohamed, SA Mostafa – Stats, 2019 – mdpi.com
… This research aims to bring together elements from philosophy and psychology to be examined by computational corpus linguistics methods in a way that promotes the use of Natural Language Processing for the Humanities …

Nominal coreference resolution for Polish
M Ogrodniczuk – Poznan Studies in Contemporary Linguistics, 2019 – degruyter.com
Jump to Content Jump to Main Navigation Publications. Subjects. Architecture and Design Arts Asian and Pacific Studies Business and Economics Chemistry Classical and Ancient Near Eastern Studies Computer Sciences Cultural …

Potential effects of chatbot technology on customer support: A case study
T Nguyen – 2019 – aaltodoc.aalto.fi
… Even if ALICE was unable to pass the Turing test, it built the foundation for the development of Artificial Intelligence Markup Language (AIML), which is … that IBM faced: (1) the broadness of the questions with rich and varied natural language expressions; (2) the requirement …

Towards the Learning, Perception, and Effectiveness of Teachable Conversational Agents
N Chhibber – 2019 – uwspace.uwaterloo.ca
… 19 3.3.2 Dialog System … Specifically, we investigate whether these teachable agents reliably learn from natural language conversations, how the teaching process affects … to adjust the number of features or threshold values for each node in the decision tree, and interactively …

Cognitive Computing Recipes
A Masood, A Hashmi – Springer
… 4 Current State of Machine-Learning & Deep-Learning Platforms ….. 6 Building a Business Case for Artificial Intelligence ….. 8 Natural-Language Understanding & Generation ….. 8 …

Automated Essay Scoring: Argument Persuasiveness
Z Ke – 2019 – utd-ir.tdl.org
… Nevertheless, it continues to draw a lot of attention in the natural language processing community in part because of its commercial and educational values as well as the … score written text, is one of the most important educational applications of natural language pro …

Strategies for online personalised nutrition advice employed in the development of the eNutri web app
RZ Franco, R Fallaize, F Hwang… – Proceedings of the …, 2019 – cambridge.org
… This decision tree was subsequently auto- mated(19), but their details have not been … Natural language processing has also been used(45–47), but still without significant results as a … Artificial intelligence approaches, such as case-based reasoning, have also been proposed for …

Real world user model: Evolution of user modeling triggered by advances in wearable and ubiquitous computing
F Cena, S Likavec, A Rapp – Information Systems Frontiers, 2019 – Springer
… 2 Background on User Modeling. A User Model (UM) in Artificial Intelligence is a data structure with the characteristics of a particular user in a certain moment in time … 2002), or supervised approaches (eg, decision trees, Naïve Bayesian classifier Zhu et al …

Usability evaluation of spoken humanoid embodied conversational agents in mobile serious games
D Korre – 2019 – era.ed.ac.uk
… and emotions. Despite these theoretical advantages, according to recent studies, the interaction with spoken dialogue systems, either in the form of an embodied agent … HCI: human computer interaction NLI: natural language interaction AI: artificial intelligence …

Text mining in education
R Ferreira?Mello, M André, A Pinheiro… – … : Data Mining and …, 2019 – Wiley Online Library
… The keywords used were: “Text Mining in Education,” “Natural Language Processing in Education” and “Writing Analytics … as Conferences on Intelligent Tutoring Systems, Learning Analytics and Knowledge, Advanced Learning Technologies, Artificial Intelligence in Education …

Intelligent Asset Management
F Xing, E Cambria, R Welsch – 2019 – Springer
… posture, behavior, physiology), and affective interfaces and applications (dialogue systems, games, learning … time, both of us were interested in working on using natural language processing techniques … area where knowledge in both finance and artificial intelligence would be …

Refined distributed emotion vector representation for social media sentiment analysis
YC Chang, WC Yeh, YC Hsing, CA Wang – Plos one, 2019 – journals.plos.org
… For this reason, in recent years, natural language processing (NLP) technology has been widely applied to social media data analysis … tasks have successfully benefited from word embedding, such as sentiment analysis [31], named entity recognition [32], and dialog systems [33 …

Expert systems in developing countries: practice and promise
CK Mann – 2019 – books.google.com
… The Rise of the Expert Company: How Visionary Companies are Using Artificial Intelligence to Achieve Higher Productivity and Profits … the term “firing” is used to refer to a decision by the inference engine to use a particular rule or rule sequence) 3. A dialog system to connect …

Speech recognition based strategies for on-line Computer Assisted Language Learning (CALL) systems in Basque
IO Sustaeta – 2019 – pdfs.semanticscholar.org
Page 1. Speech recognition based strategies for on-line Computer Assisted Language Learning (CALL) systems in Basque Igor Odriozola Sustaeta Aholab Signal Processing Laboratory Department of Communication Engineering Advisors: Inma Hernáez and Eva Navas …

Speech recognition based strategies for on-line Computer Assisted Language Learning (CALL) systems in Basque
I Odriozola Sustaeta – 2019 – addi.ehu.eus
Page 1. Speech recognition based strategies for on-line Computer Assisted Language Learning (CALL) systems in Basque Igor Odriozola Sustaeta Aholab Signal Processing Laboratory Department of Communication Engineering Advisors: Inma Hernáez and Eva Navas …

Neural Name Tagging for Low-Resource Languages
B Zhang – 2019 – blender.cs.illinois.edu
… Semantic web. Their early foreseeing contributions to the field of Artificial Intelligence still … standing problems in the field of Natural Language Processing (NLP). Information Extrac … in industrial applications, such as Question Answering and Dialogue System …

A multimodal approach to sarcasm detection on social media
D Das – 2019 – researchgate.net
… Page 91 8.1.2 Natural Language Processing, Understanding, and Generation Tasks Page 92 … 12 Page 24. 3 HOW HUMANS DETECT SARCASM The basic idea behind machine learning based systems, or artificial intelligence in gen- eral, is mimicking how humans operate …

Designing and evaluating recommender systems with the user in the loop
M Jugovac – 2019 – 129.217.131.68
Page 1. Designing and Evaluating Recommender Systems With the User in the Loop Dissertation zur Erlangung des Grades eines Doktors der Naturwissenschaften der Technischen Universität Dortmund an der Fakultät für Informatik von Michael Jugovac Dortmund 2019 …

Towards the Automatic Classification of Student Answers to Open-ended Questions
JG Alvarado Mantecon – 2019 – ruor.uottawa.ca
… 14 2.2.5 Artificial Intelligence Techniques for ASAG ….. 15 … DBN Deep Belief Network DT Decision Tree DUM Dummy Classifier EE Entity Embedding … NB Naïve Bayes NLP Natural Language Processing NLTK Natural Language Toolkit …

Deep multigrained cascade forest for hyperspectral image classification
X Liu, R Wang, Z Cai, Y Cai… – IEEE Transactions on …, 2019 – ieeexplore.ieee.org
… of a discriminative deep forest, which is to assign weights to decision trees in a … for Hyperspectral Image Classification Deep learning is a hot topic in artificial intelligence, and it has … in image recognition [27], [28] speech recognition [29], [30], and natural language process- ing [31 …

A fuzzy logic approach to reliable real-time recognition of facial emotions
K Bahreini, W van der Vegt, W Westera – Multimedia Tools and …, 2019 – Springer
… The combination of posture and conversational dialogue systems reveals a modest amount of … Such analysis is called sentiment analysis and uses natural language processing techniques for … such as neural networks, Bayesian networks, and decision trees would require …

Computer Vision beyond the visible: Image understanding through language
A Salvador – 2019 – tdx.cat
… image generation [20, 224] image captioning [201, 215] or visual dialog systems [45 … closer to research com- munities studying other data modalities, such as natural language or audio … of models in machine learning, such as support vector machines, decision trees and bayesian …

Reinforcement learning in healthcare: A survey
C Yu, J Liu, S Nemati – arXiv preprint arXiv:1908.08796, 2019 – arxiv.org
… achievements in general- ization, representation and efficiency in recent years, leading to its increasing applicability to real-life problems in playing games, robotics control, financial and business management, autonomous driving, natural language processing, computer vision …

How affordances of chatbots cross the chasm between social and traditional enterprise systems
E Stoeckli, C Dremel, F Uebernickel, W Brenner – Electronic Markets, 2019 – Springer
… 2011), chatterbot (Mauldin 1994), and dialogue system (Litman and Pan 2002 … Natural language processing capabilities can now be used to extract meaning from textual input and … chatbots operating on fixed rule-based pattern matching and simple decision trees (Schuetzler et …

The development of an automatic pronunciation assistant
TJ Sefara – 2019 – ulspace.ul.ac.za
… al., 2012), and document processing systems (Shukla et al., 2016). LID is a problem of discovering the identity of the natural language of a given … mobile smartphones, computers, internet-based services, and dialogue systems …

Computer Vision beyond the visible: Image understanding through language
A Salvador Aguilera – 2019 – upcommons.upc.edu
… image generation [20, 224] image captioning [201, 215] or visual dialog systems [45 … closer to research com- munities studying other data modalities, such as natural language or audio … of models in machine learning, such as support vector machines, decision trees and bayesian …

Myanmar Language Continuous Speech Recognition Using Convolutional Neural Network (CNN)
AN Mon – 2019 – onlineresource.ucsy.edu.mm
… supervisor, Dr. Win Pa Pa, Professor, Natural Language Processing Lab., the … construct the phonetic decision tree so that to develop more sophisticated tone modeling … telephony, in case spoken dialogue systems for entering digits, recognizing to receive …

The Impostor: Exploring narrative game design for learning Korean as a foreign language
T Engström – 2019 – aaltodoc.aalto.fi
Page 1. 1 Exploring narrative game design for learning Korean as a foreign language Master’s Thesis Tom Engström The imposTor Page 2. 2 ! Aalto-yliopisto, PL 11000, 00076 AALTO www.aalto.fi Taiteen maisterin opinnäytteen tiivistelmä …

Deep learning for multi-class identification from domestic violence online posts
S Subramani, S Michalska, H Wang, J Du… – IEEE …, 2019 – ieeexplore.ieee.org
… algorithms [42], [43] for text clas- sification tasks are: Support Vector Machine (SVM), Logistic Regression (LR), Decision Trees (DT), Naive … The successful applications of Deep Learning have also been observed in Natural Language Processing (NLP) tasks, including Part-of …

A comparative study of social bot classification techniques
F Örnbratt, J Isaksson, M Willing – 2019 – diva-portal.org
… Many such machine learning algorithms exist; Decision tree, Random forests, Naïve Bayes, K-Nearest Neighbour, Support Vector Machines or k-means … Web Robots (crawlers) ? Chatbots (natural language based dialog system) …

Building Chatbots with Python
S Raj, S Raj, Karkal – 2019 – Springer
… 21 How Does a Decision Tree Help … 121 Understanding More on Rasa Core and Dialog System ….. 122 … Nitin Solanki has extensive experience in Natural Language Processing, Machine Learning, and Artificial Intelligence Chatbot development …

Ai & Quantum Computing For Finance & Insurance: Fortunes And Challenges For China And America
LDK Chuen, S Paul – 2019 – books.google.com
… The series covers several increasingly important new areas such as the fourth industrial revolution, Inter- net of Things (IoT), blockchain technology, artificial intelligence (AI) and many other forces of disruption and breakthroughs that shape today’s realities of the economy …

Question Generation with Adaptive Copying Neural Networks
X Lu – 2019 – curve.carleton.ca
… For example, dialog systems in chatbots are currently drawing significant attention … Readers familiar with deep learning and natural language processing may skip this chapter … Deep learning is an artificial intelligence technology that helps computers learn …

Personality-based recommendation: human curiosity applied to recommendation systems using implicit information from social networks
A Menk Dos Santos – 2019 – riunet.upv.es
… Development of RSs is a multi-disciplinary effort which involves profes- sionals from different fields, such as artificial intelligence, human-computer interaction, data mining, statistics, decision support systems, marketing, con- sumer behaviour and psychology [9]. As a …

Future Research.
RH Reussner, M Goedicke, W Hasselbring… – 2019 – library.oapen.org
… Organization of the Whole Range of Software Qualities This area sketched above can be seen as a special case of the situation wherein a soft- ware development code, specifications, models, and natural language descriptions have … In: Artificial Intelligence and Law 17.1 (Nov …

Representation Learning for Information Extraction
E Amjadian – 2019 – curve.carleton.ca
… 1.1 The Tasks to Tackle Statistical Natural Language Processing (henceforth sNLP) is a subfield of Computer Science and Artificial Intelligence that employs statistics and machine learning at its core to enable machines to model, understand, and generate natural language …

Designing for Trust
M Dagli – 2019 – kilthub.cmu.edu
… text, interact interchangeably as chatbots, virtual agents, virtual assistants, virtual companions, avatars, ‘artificial intelligence’ or … program that interacts with humans in natural language.13 … In Proceedings of the 2010 Workshop on Companionable Dialogue Systems (CDS ’10) …

Adversarial learning in statistical classification: A comprehensive review of defenses against attacks
DJ Miller, Z Xiang, G Kesidis – arXiv preprint arXiv:1904.06292, 2019 – arxiv.org
… generalized linear classifiers (eg, support vector machines (SVMs) [89]). Another widely used classifier structure is a decision tree [13]. For a review of various classification methods, see [24]. DNNs perform nonlinear processing via multiple layers of computational neurons …

Towards Universal End-to-End Affect Recognition from Multilingual Speech by ConvNets
D Bertero, O Kampman, P Fung – arXiv preprint arXiv:1901.06486, 2019 – arxiv.org
… The classifier choice ranged from basic supervised classifiers such as SVM [26], [30] and decision trees [31], to more complex deep learning structures such as DNN [32], CNN [33], ELM [29] or LSTM in the case of continuous emotion detection [34] …

A cognitive IoE (Internet of Everything) approach to ambient-intelligent smart space.
GS Jamnal – 2019 – core.ac.uk
… SICSA PhD conference, University of Dundee, 27-28 June, 2017. 6. Artificial Intelligence: Cognitive IoE(Internet of Everything) Approach to Ambient-Intelligent … ACM Ambient Cognition Model AEM Ambient Expert Model AI Artificial Intelligence ANN Artificial Neural Network …

Conversational Agent for Health Coaching
A JUMAAH – 2019 – researchgate.net
Page 1. Doctoral Dissertation Doctoral Program in Information Engineering & Computer Science (30thcycle) CONVERSATIONAL AGENT FOR HEALTH COACHING Design, Development and Pilot Evaluation of A Conversational Agent Assisted Coaching Platform …

Improving Software Defect Assignment Accuracy with the LSTM and Rule Engine Model
R Zhu – 2019 – search.proquest.com
… Recently thanks to the rapid development of artificial intelligence especially in the area of deep learning … language processing … by LSTM, decision trees, and expert systems theories on software defect assignment, the chapter …

An Automated Privacy Information Detection Approach For Online Social Media
J Wu – 2019 – orapp.aut.ac.nz
… In Proceedings of the 33rd Annual Conference of Japanese Society for Artificial Intelligence (JSAI), Japanese Society for Artificial Intelligence (JSAI) (Accepted) … Machine learning begins from the early research in Artificial Intelligence, which gives the computer

Quantifying Internal Representation for Use in Model Search
NT Blanchard – 2019 – search.proquest.com
Page 1. QUANTIFYING INTERNAL REPRESENTATION FOR USE IN MODEL SEARCH A Dissertation Submitted to the Graduate School of the University of Notre Dame in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Nathaniel T. Blanchard …

Rule-Based Interactive Assisted Reinforcement Learning
A Bignold – researchgate.net
… xii Page 14. 7.9 Steps per episode for non-persistent and persistent agents using informative advice. . . . . 125 8.1 Binary Decision Tree. . . . . 131 8.2 Ripple-Down Rules (Debbie Richards, 2009) …

Deep Neural Networks for Human Activity Recognition with Wearable Sensors
M Zeng – 2019 – search.proquest.com
… (SVM) [76], Nave Bayes (NB) and Decision Tree (DT) [14 … speech recognition (ASR), natural language processing (NLP), and search behavior modeling, which have inspired … machine translation [11, 116], text summarization [156, 26], dialogue systems [177], question-answering …