Random Forest & Chatbots 2019


See also:

AI Algorithms Meta Guide


Arabic Dialogue Act Recognition for Textual Chatbot Systems
A Joukhadar, H Saghergy, L Kweider… – Proceedings of The First …, 2019 – aclweb.org
Arabic Dialogue Act Recognition for Textual Chatbot Systems … speech act categories: Logistic Regression, SVM, Multi- nomial NB, Extra Trees Classifier, Random Forest Classifier … In general, chat bot systems can be composed of three basic components: Natural Language Un …

Survey On Chat Bot System For Cancer Patient
P Nehul, B Lohar, U Jagtap, S Rajurkar, G Virkar – oaijse.com
… Keywords: Chat-Bot, Natural Language Processing, Health-Care, Random-Forest … The techniques of Chatbot design are still a matter for debate and no common approach has yet been identified … particular applications. General-purpose Chatbots need …

Towards Emotion Intelligence in Neural Dialogue Systems
C Huang – 2019 – era.library.ualberta.ca
… 12 2.4.3 Random Forest … With the recent development of Deep Learning (DL), building open-domain social chatbots has received much more attention from the research community … social textual chatbot XiaoIce [104] states that a mature chatbot should be …

The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning
N Shi, Q Zeng, R Lee – arXiv preprint arXiv:2009.13984, 2020 – arxiv.org
… The above chatbots discussed are basically adopting the Seq2Seq model, it gets good performance for … Transfer Transfo we used as chatbot in our agent is a language system combining … On the contrary, random forest, which consist of lots of decision trees has higher accuracy …

Chatbot Components and Architectures
B Galitsky – Developing Enterprise Chatbots, 2019 – Springer
… The topic detector uses a text classifier such as random forest (Xu et al … 2009). For example, if the entity is a movie director, the chatbot retrieves the director’s gender, age, acted films list; if the entity is a city, the chatbots gets its location, which country it belongs to, and …

Deep Learning Techniques for Implementation of Chatbots
SPR Karri, BS Kumar – 2020 International Conference on …, 2020 – ieeexplore.ieee.org
… a chatbot in the educational domain develops, and data is processed using the random forest algorithm … Balakrishnan and S.Reshmi proposed,” Implementation of an inquisitive chatbot for database supported knowledge bases.” Generally, chatbots only respond to …

Hybrid Supervised Reinforced Model for Dialogue Systems
C Miranda, Y Kessaci – arXiv preprint arXiv:2011.02243, 2020 – arxiv.org
… In section 2, we give a brief introduction of reinforcement learning for chatbots by defining the two main components of … 3 Chatbot environment … of certain dimensions to particular boolean vector of TC-Bot current slots vector, we trained multiple random forest classifiers with a One …

Developing Enterprise Chatbots
B Galitsky – 2019 – Springer
… This book is intended to substantially improve chatbot engineering, providing the solid scientific background for building sophisticated dialogue systems. In particular, this book educates chatbot developers on building search engines for chatbots with linguistically-enabled …

A Review of Current Trends in the Development of Chatbot Systems
TP Nagarhalli, V Vaze, NK Rana – 2020 6th International …, 2020 – ieeexplore.ieee.org
… for College Website [19] Content-Oriented User Modeling for Personalized Response Ranking in Chatbots [20] … Chatbot system’s target audience or Domain of study … algorithm used Not mentioned Not mentioned Deep Neural Network Not mentioned Random forest and SVM …

Improving Task-Oriented Dialogue Systems In Production with Conversation Logs
A Jacovi, OB El, O Lavi, D Boaz, D Amid, I Ronen… – ceur-ws.org
… The dialogue system fails, causing an esca- lation to a human who resolves the case; The system then … Task- oriented1 dialogue systems are not only concerned with maintain- ing coherent interaction with another party (eg, chit-chat agents, or chatbots), but also …

IntelliBot: A Dialogue-based chatbot for the insurance industry
M Nuruzzaman, OK Hussain – Knowledge-Based Systems, 2020 – Elsevier
… The next sub-section presents a summary of each technique and a comparison is conducted to identify the gaps from the perspective of meeting the requirements needed from response-generating chatbots. 3.1 … TF-IDF [43] with random forest, A chatbot to assist Q&A on …

Automated scoring of chatbot responses in conversational dialogue
SK Yuwono, B Wu, LF D’Haro – … Workshop on Spoken Dialogue System …, 2019 – Springer
… In this paper, we have addressed the task of automated scoring of chatbot responses or … In: WOCHAT: workshop on chatbots and conversational agent technologiesGoogle Scholar. 3. Banchs RE, Li … Xu B, Guo X, Ye Y, Cheng J (2012) An improved random forest classifier for text …

Topic Judgment Helps Question Similarity Prediction in Medical FAQ Dialogue Systems
L Zhu, X Dai, Q Huang, H Xiang… – … Conference on Data …, 2019 – ieeexplore.ieee.org
… chatbots) [4]. A task-oriented system is specific to a domain ie, it solves problems in … relevance with healthcare or our knowledge base, our system will call Turing chatbot API or … for our system, we compared different classifiers using all the features (Random forest, AdaBoost, etc …

Natural Language Understanding in Smartdialog: A Platform for Vietnamese Intelligent Interactions
NTT Trang, NH Ky, H Son, NT Hung… – Proceedings of the 2019 …, 2019 – dl.acm.org
… such as virtual assistant eg Siri, Alexa, Cortana, Google Assistant [2], chat bot eg Chatfuel … Beside the Random Forest model with BOW and TF-IDF implementation, we have … [3] S. Janarthanam, Hands-On Chatbots and Conversational UI Development: Build chatbots and voice …

Passive Diagnosis of Mental Health Disorders Incorporating an Empathic Dialogue System
F Delahunty, M Arcan, R Johansson – 2019 – thesiscommons.org
… 3Commonly known as chatbots … par- ticipants from the public domain who had a short conversation with our proposed empathic dialogue system … To evaluate Hypothesis 2, we randomly allocated recruited participants to have conversations with one of two dialogue systems …

A Survey on Chat-Bot system for Agriculture Domain
PY Niranjan, VS Rajpurohit… – 2019 1st International …, 2019 – ieeexplore.ieee.org
… Fig.1 Flow of chat-Bot system Question Analysis … 2014 III. LITERATURE SURVEY Literature survey has been discussed in this section to highlight the work carried out till now in chatbot system … Experiment was conducted with Naïve-Bayes, random forest and decision trees …

A dialogue-based annotation for activity recognition
T Mairittha, N Mairittha, S Inoue – Adjunct Proceedings of the 2019 ACM …, 2019 – dl.acm.org
… 3.1 A dialogue-based annotation Dialogue system, also known as a conversational agent, virtual agent, or chatbot [12]. It is the system that communicates with … For classification, Random Forest [3], an ensemble learning method is exploited … From chatbots to dialog systems …

What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations
M Ku?ba, P Biecek – arXiv preprint arXiv:2002.05674, 2020 – arxiv.org
… We implement this particular system for the random forest model trained on Titanic dataset but this approach might be transferred successfully to other models and datasets … This exploration is enabled by the open-ended character of the chatbot … 2. Dialogue system …

Intent Detection-Based Lithuanian Chatbot Created via Automatic DNN Hyper-Parameter Optimization
J Kapo?i?t?-Dzikien? – Frontiers in Artificial Intelligence and …, 2020 – books.google.com
… RC, K-Nearest Neighbors, Multilayer Perceptron, Passive Aggressive–PA, Random Forest–RF, Linear … Katins J. An Information Retrieval-based Approach for Building Intuitive Chatbots for Large … Z, Zhou J. DocChat: An Information Retrieval Approach for Chatbot Engines Using …

2. Keynote Speech Variation in spoken pluricentric languages: insights from large corpora and challenges for speech technology
MA Decker – Book of Abstracts – pluricentriclanguages.org
… to-speech synthesis, automatic speech transcription and translation, information retrieval, dialog systems, chatbots … 80 Classification using Support Vector Machine (SVM), Random Forest (RF), Multilayer … T. Habib and SU Rahman,“Spoken dialog system framework supporting …

Remote cardiovascular health monitoring system with auto-diagnosis
B Bhattacharya, S Mohapatra… – … on Vision Towards …, 2019 – ieeexplore.ieee.org
… Chatbots are generally used in dialog systems for various practical purposes and use sophisticated Natural Language Processing (NLP … Figure 5. Chatbot Questionnaire … D.2. Random Forest Classifier (RF) It has mixture of tree classifiers where every single classifier is made …

Twitter Bots and the Swedish Election
J Fernquist, L Kaati, R Schroeder, N Akrami… – … Source Intelligence and …, 2020 – Springer
… chatbots (human-computer dialog system which operates … Random forest is the classification algorithm that has been proven to give the best performance for bot detection for the supervised problem when several different classifiers have been tested [14, 18, 19] …

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 … Moreover, it is feasible to automatically generate pairs of questions and answers by mining datasets in the fields of Question Answering and dialog systems …

Evaluation of Chatbot Prototypes for Taking the Virtual Patient’s History.
A Reiswich, M Haag – dHealth, 2019 – books.google.com
… algorithms were applied, which were already integrated in scikit- learn, including: Random Forest (RF), Naïve … Lokman, JM Zain, FS Komputer and K. Perisian, Designing a Chatbot for diabetic … HN Io and CB Lee, Chatbots and Conversational agents: A bibliometric analysis, in …

Intelligent Forecasting System for NPP’s Energy Production
O Chornovol, G Kondratenko, I Sidenko… – 2020 IEEE Third …, 2020 – ieeexplore.ieee.org
… Peculiarities of human machine interaction for synthesis of the intelligent dialogue chatbot,” 10th IEEE … [25] A. Liaw and M. Wiener, “Classification and regression by random forest,” in R … [27] P. Kushneryk, Y. Kondratenko, and I. Sidenko, “Intelligent dialogue system based on …

Ranking of Potential Questions
L Schricker, T Scheffler – Proceedings of the 57th Annual Meeting of the …, 2019 – aclweb.org
… An additional inspection of the best perform- ing Random Forest model’s features by impor- tance showed the three ordering constraint fea … these linguis- tic theories, but might also be useful in dialogue generation applications, eg for machine dialogue systems and chatbots …

Open Domain Conversational Chatbot
V Deshmukh, SJ Nirmala – International Conference on Information …, 2019 – Springer
… Machine learning model: XGBoost, neural networks, random forest and SVM are the machine learning models … There is a huge range of chatbots identified based on the learning capacity … J., Chen, P., Zhou, M.: DocBot: an information retrieval approach for chatbot engines using …

Samvaadhana: A Telugu Dialogue System in Hospital Domain
SR Duggenpudi, KSS Varma, R Mamidi – … of the 2nd Workshop on Deep …, 2019 – aclweb.org
… Random Forest+BOW+Unigrams 95.348% Random Forest+BOW+Bigrams 90.697% Random Forest+TFIDF+Bigrams 95.348% Random Forest+TFIDF+Both … Spoken dialogue system using recognition of user’s feedback for rhythmic dialogue … A neural-network based chat bot …

On the Impact of Voice Encoding and Transmission on the Predictions of Speaker Warmth and Attractiveness
LF Gallardo, R Sanchez-Iborra – ACM Transactions on Knowledge …, 2019 – dl.acm.org
… is on assessing how two well-known classification algorithms, namely, Random Forest (RF) classifier … growing interest in human-computer interactions and spoken dialog systems has been … Chatbots are becoming increasingly predominant, especially for customer service and …

Bootstrapping conversational agents with weak supervision
N Mallinar, A Shah, R Ugrani, A Gupta… – Proceedings of the AAAI …, 2019 – aaai.org
… areas for future work to improve this new approach for developing chatbots, as well … the propagated labels with marginal probabili- ties are used to train random forest regression models … Moreover, chatbot development of- ten requires revising the intents as end users’ behaviors …

FastText-Based Intent Detection for Inflected Languages
K Balodis, D Deksne – Information, 2019 – mdpi.com
… require a large amount of training data, which is not the typical case for chatbots … Here, we compare the results of our best model with other popular chatbot services on the … The model was trained using a random forest classifier that learned to rate normalization candidates for …

Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations
W Cai, L Chen – Proceedings of the 28th ACM Conference on User …, 2020 – dl.acm.org
… authors identified four most-frequent user intents in a shopping chatbot (ie, recommendation … they may want to achieve when they interact with a dialogue system [15 … Logistic Regression, Support Vector Machine (SVM), Naive Bayes, Decision Tree, Random Forest, XGBoost, and …

Assessing the Reviving Risks while using the Manufacturing Resource Planning system at agribusiness enterprises
O Ponochovna, V Piliavskyi, P Makarenko – 2019 – dspace.pdaa.edu.ua
… parametric machine learning methods has not provided satisfactory accuracy, ie, the random forest ensemble as … of companies have implemented or are planning to implement a chat-bot in the … findings have not slowed down the rapid im-plementation of chat-bots online, and …

A comparative study of social bot classification techniques
F Örnbratt, J Isaksson, M Willing – 2019 – diva-portal.org
… Keywords: manual bot classification, social bot, metadata, machine learning, supervised learning, unsupervised learning, random forest, k-means Page 3. Contents 1 Introduction 1 … Web Robots (crawlers) ? Chatbots (natural language based dialog system) …

User intent prediction in information-seeking conversations
C Qu, L Yang, WB Croft, Y Zhang, JR Trippas… – Proceedings of the 2019 …, 2019 – dl.acm.org
… can only be extracted from the metadata of the forum, such as user authority level or answer votes, so that our method can be applicable to dialog systems … We adopt classic ML methods, including Naive Bayes classifier, SVM, random forest, and AdaBoost as baseline classifiers …

Rehabilitace paže pomocí detekce pohybu ve videu
M Ryba – 2020 – dspace5.zcu.cz
… Various proposed solutions to the problem, such as usage of Virtual Reality (VR) (§2.5.1) or dialogue systems (§2.5.2), are thus … Dialogue system is a special type of software designed to interact with its users by simulating human conversation through text or synthesised voice …

Influence of Time and Risk on Response Acceptability in a Simple Spoken Dialogue System
A Partovi, I Zukerman – Proceedings of the 20th Annual SIGdial Meeting …, 2019 – aclweb.org
… describe a longitudinal user study con- ducted in the context of a Spoken Dialogue System for a … (2016) produced dialogue contributions of chat- bots; and Serban … 1 of our experiment (Section 3):7 Naïve Bayes, Support Vector Machines, Decision Trees, Random Forest (RF) and …

Towards Interactive Advisory System for Security Export Control
A Obayashi, R Rzepka23 – researchmap.jp
… We developed an interactive Slack chatbot11 prototype to show the feasibil- ity of the new … the system’s questions, also research pa- pers can be one sent to the dialog system to provide … NB 0.14 0.75 Linear SVC 0.56 0.89 Logistic Regression 0.25 0.86 Random Forest 0.07 0.14 …

Chatbot Interaction with Artificial Intelligence: Human Data Augmentation with T5 and Language Transformer Ensemble for Text Classification
JJ Bird, A Ekárt, DR Faria – arXiv preprint arXiv:2010.05990, 2020 – arxiv.org
… October 14, 2020 ABSTRACT In this work, we present the Chatbot Interaction with Artificial Intelligence (CI-AI) framework as an approach to the training of deep learning chatbots for task classification. The intelligent system …

A prior case study of natural language processing on different domain
J Shruthi, S Swamy – International Journal of Electrical and …, 2020 – search.proquest.com
… The presenting chatbot technique follow the statement of text information that’s text by user in the … It can be utilized as a text interface or like a linguistic dialog system … disorder which based on AI and neural network, maximum entropy, support vector machine and random forest …

A prior case study of natural language processing on different domain.
S Swamy – International Journal of Electrical & Computer …, 2020 – search.ebscohost.com
… The presenting chatbot technique follow the statement of text information that’s text by user in the … It can be utilized as a text interface or like a linguistic dialog system … disorder which based on AI and neural network, maximum entropy, support vector machine and random forest …

Mining Text in Incident Repositories: Experiences and Perspectives on Adopting Machine Learning Solutions in Practice
J Misra, S Podder – … 34th IEEE/ACM International Conference on …, 2019 – ieeexplore.ieee.org
… Pilot Studies and Observations: A Random Forest model was built with hyperparameter optimization on number of trees and max number of features using grid-search. Model was trained on each incident data-set using ‘bag-of-words’ with tf-idf weighing as feature space …

Toward Artificial Social Intelligence: A Semi-supervised, Split Decoder Approach to EQ in a Conversational Agent
S Shankar, V Sruthi, V Satyanarayana… – Advances in Artificial …, 2020 – Springer
… For this, random forest classifiers were used since tree-based models are more immune to class … He Xd, Li D (2018) From Eliza to Xiaoice: challenges and opportunities with social chatbots … J, Chu W (2017) AliMe chat: a sequence to sequence and rerank based chatbot engine …

Socially-Aware Dialogue System
R Zhao – 2019 – lti.cs.cmu.edu
… However, social chatbots fall short in replicating the interpersonal function of communication … SAPA) This chapter reviews our knowledge-inspired socially-aware dialogue system in a … recognition of conversational strategies in the service of a socially-aware dialog system …

Enhancing sentient embodied conversational agents with machine learning
D Tellols, M Lopez-Sanchez, I Rodríguez… – Pattern Recognition …, 2020 – Elsevier
… accompanied by the emergence of a number of tools for chatbot development, including … is a widely used keyword matching mechanism used to implement chatbots (eg, ALICE … on 5 different Machine Learning algorithms: Support Vector Machines; Random Forest; Gradient Tree …

MSc in Computer Science
RB Sulaiman – researchgate.net
… CHAPTER INFORMATION IN THIS CHAPTER ? Overview ? Chatbot system ? Chatbot technology ? Types of chatbot ? Comparison of chatbots ? Functions of chatbot ? Syntactic analysis ? Vector space model ? Machine learning models ? Support vector machines (SVM) …

Developing Amaia: A Conversational Agent for Helping Portuguese Entrepreneurs—An Extensive Exploration of Question-Matching Approaches for Portuguese
J Santos, L Duarte, J Ferreira, A Alves, HG Oliveira – Information, 2020 – mdpi.com
… 2. Related Work. Dialogue systems typically exploit large collections of text, often including conversations … In opposition to generative systems, IR-based dialogue systems do not handle very well requests for which there is no similar text in the corpus …

Automated Conversation Review to Surface Virtual Assistant Misunderstandings: Reducing Cost and Increasing Privacy.
I Beaver, A Mueen – AAAI, 2020 – researchgate.net
… dataset as human voting data is added. Our final selection for the voting classifier was a Random Forest model with 30 estimators, which required on average 2 seconds to train. Voting Classifier Evaluation Having selected a …

Artificial Intelligence acceptance: morphological elements of the acceptance of Artificial Intelligence
MM Figueiredo – 2019 – repositorio.ucp.pt
… Some examples of supervised learning algorithms are logistic regression, naive bayes, random forest and artificial neural networks4 … Duolingo is a very well-known chatbot that allows people to learn and … to chatbots ranging from virtual agents and dialogue systems to machine …

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

A Guide to the NeurIPS 2018 Competitions
R Herbrich, S Escalera – The NeurIPS’18 Competition, 2020 – Springer
… pivotal in demonstrating the practicability of deep neural networks as well as XGBoost and random forest (which were … The aim of this challenge was to establish a concrete scenario for testing chatbots that aim to … The winning dialogue systems was chosen based on these scores …

InPHYNet: Leveraging attention-based multitask recurrent networks for multi-label physics text classification
V Udandarao, A Agarwal, A Gupta, T Chakraborty – Knowledge-Based Systems – Elsevier
… to-use form that can be utilized to build a question-answering physics chatbot, we prepared … an increasing focus on building end-to-end QA based interactive dialog systems for facilitating … of work has been pivoted on the Student Response Analysis (SRA) part of a dialog system …

Recognition of cooking activities through air quality sensor data for supporting food journaling
F Gerina, SM Massa, F Moi… – Human-centric …, 2020 – hcis-journal.springeropen.com
… Indeed, we believe that these technologies, possibly coupled with automatic dialog systems, may provide adequate solutions for unobtrusive and privacy-conscious context recognition, as well as user engagement for supporting diet data acquisition on the long term …

Cap. 1. ARTIFICIAL INTELLIGENCE APPLICATIONS AND TOOLS IN HIGHER EDUCATION: AN OVERVIEW
N Romanov, MI Cluci, ID Anastasiei… – … educa?iei doctorale în … – sesyr.feaa.uaic.ro
… which could be related to personalized learning, students’ personal data, chat bots and even … can learn a foreign language more efficiently with the use of human-machine dialogue systems … Three major algorithms were used: Random Forest (RF), Logistic Regression and ANN …

Medical Instructed Real-Time Assistant for Patient with Glaucoma and Diabetic Conditions
UU Rehman, DJ Chang, Y Jung, U Akhtar… – Applied Sciences, 2020 – mdpi.com
… calls, and many more things [1]. Many applications from different domains currently have their own built-in virtual assistants such as televisions [2], mobile devices [3], vehicles [4], and the Internet of things [5,6]. The virtual assistant is also known as a chatbot, dialogue manager …

Detecting Abuse on the Internet: It’s Subtle
S Bagga – 2020 – search.proquest.com
… al. [12] incorporate user attribues such as age, inter-arrival time, and network- based attributes such as popularity, centrality etc. They use Random Forest algorithm to classify a Twitter user as normal, aggressive, bullying, or spam- ming. Gao et al …

A context-aware conversational agent in the rehabilitation domain
T Mavropoulos, G Meditskos, S Symeonidis… – Future Internet, 2019 – mdpi.com
… in Reference [10], where emphasis is placed on the role of chatbots beyond patient monitoring; it focuses on patient–doctor interaction over a chatbot-driven telemedicine … An essential unit of any dialogue system is the module that manages the interaction between the …

The Impact of Toxic Replies on Twitter Conversations
N Salehabadi – 2019 – rc.library.uta.edu
… Natural Language Processing (NLP) Techniques are used in Question-Answering, Chatbots and dialog systems. Generative models are state of the art for generating text, and … SVM, and random forest (Davidson et al., 2017), and (2) deep learning methods (Koratana et …

Computational Sarcasm Processing: A survey
KGP Bhattacharyya – cfilt.iitb.ac.in
… a sarcasm generation module (Sar- casmBot) for chatbots and mention that integrat- ing a sarcasm generation module allows existing chatbots to become … to train a variety of classi- fiers including linear SVM, logistic regression, de- cision tree, random forest and naive bayes …

Integrating artificial and human intelligence: a partnership for responsible innovation in biomedical engineering and medicine
K Dzobo, S Adotey, NE Thomford… – OMICS: A Journal of …, 2020 – liebertpub.com
… Weiner et al., 2018). The authors trained and used the random forest machine learning algorithm to identify metabolite signatures that can foretell progression of TB on African samples (Weiner et al., 2018). Such studies, utilizing …

Developing Emotion-Aware Human–Robot Dialogues for Domain-Specific and Goal-Oriented Tasks
JY Huang, WP Lee, CC Chen, BW Dong – Robotics, 2020 – mdpi.com
… Essentially, the dialogue system includes a knowledge base (ie, dataset) with organized domain questions … In the task-oriented dialogue systems, the most critical component is the goal … to the open-domain conversation performed by the general purpose chatbots, the domain …

“I think it might help if we multiply, and not add”: Detecting Indirectness in Conversation
P Goel, Y Matsuyama, M Madaio, J Cassell – … on Spoken Dialogue System …, 2019 – Springer
… supervised machine learning algorithms—Logistic Regression, Naive Bayes, Random Forest and Support … or a general-purpose socially-aware spoken dialogue system [30] that … interpersonal communication, incorporating its detection in spoken dialogue systems may ultimately …

Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues
S Park, D Kim, A Oh – arXiv preprint arXiv:1904.00350, 2019 – arxiv.org
… Mobile-based psychotherapy programs (Mantani et al., 2017), fully automated chatbots (Ly et al., 2017; Fitzpatrick et al., 2017), and intervention through smart devices (Torrado et al., 2017) are examples … Page 7. (1) Random Forest, (2) SVM with RBF kernel …

EMMA: An Emotion-Aware Wellbeing Chatbot
A Ghandeharioun, D McDuff… – 2019 8th …, 2019 – ieeexplore.ieee.org
… Synchronous, text-based interventions, either by a human or a chat-bot, have shown … BL quadrant of Russel’s circumplex model of emotion, the chatbot would recommend an … of classifiers includ- ing Logistic Regression, Ridge, AdaBoost, Bagging, Random Forest, and Gaussian …

The Boating Store Had Its Best Sail Ever: Pronunciation-attentive Contextualized Pun Recognition
Y Zhou, JY Jiang, J Zhao, KW Chang… – arXiv preprint arXiv …, 2020 – arxiv.org
… Automatic recog- nition of humor has become an important task in the area of figurative language processing, which can benefit various downstream NLP applications such as dialogue systems, sentiment analysis, and machine translation (Melby and Warner, 1995; Augello et al …

Artificial Intelligence in the legal sector. A comparative analysis of expert and AI approaches to predicting court decisions
N Kaliazina – 2019 – pdfs.semanticscholar.org
… ones such as spell checking, keyword search, information extraction from websites, documents classification, machine translation, spoken dialogue systems, and complex question answering … Another branch of screening solutions is usually presented in the form of chatbots that …

FinBrain: when finance meets AI 2.0
X Zheng, M Zhu, Q Li, C Chen, Y Tan – Frontiers of Information Technology …, 2019 – Springer
… facial age estimation with a deep convolutional neural network (CNN) by fusing random forest and quadratic … Generally, chatbot systems can analyze customers’ goals and are highly responsive to cus- tomers with … 2017) presented a task- completion dialogue system to complete …

Diving Deep into Deep Learning: History, Evolution, Types and Applications
HCA Deekshith Shetty, MJ Varma, S Navi, MR Ahmed – researchgate.net
… Polynomial Regression Support Vector Machine (SVM) Support Vector Regression Kernel SVM Decision Tree Regression Naive Bayes Decision Tree Classification Random Forest Classification Unsupervised Learning Clustering Dimensionality Reduction K-Means Principal …

Critical Infrastructures Security: Improving Defense Against Novel Malware and Advanced Persistent Threats
G Laurenza – 2020 – iris.uniroma1.it
… 77 7.1.3 Random Forest Classifier … 73 6.10 Execution time comparison for all the tested approaches. . . . . 74 7.1 An example of how calculate leaves confidences in a Decision Tree of the Random Forest …

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

Data fusion methods in multimodal human computer dialog
Y Ming-Hao, TAO Jian-Hua – Virtual Reality & Intelligent Hardware, 2019 – Elsevier
… such as multi-level support vector machine, decision regression tree, random forest and other … tracks for the core components in developing end-to-end dialog systems based on … While in multi-modal dialog system, we need combine multimodal channel information together to …

Suicidal ideation detection in online social content
S Ji – 2020 – researchgate.net
… which was later discontinued because of privacy issues. The latter is a Facebook chatbot based on … Similarly, Ji et al. [17] compared four classification methods of logistic regression, random forest, gradient boosting decision tree, and XGBoost. Braithwaite et al …

Era of Intelligent Systems in Healthcare
S Belciug, F Gorunescu – … Decision Support Systems—A Journey to …, 2020 – Springer
The aim of this chapter is to prepare the reader for the outstanding trip that she/he embarked when starting reading this book. At first, we shall try to look for answers to some of the most…

DEEP NEURAL NETWORD BASED NATURAL LANGUAGE INFERENCE MODEL
T BEKELE – 2020 – ir.bdu.edu.et
… NLU – Natural Language Understanding RF – Random Forest RBM – Restricted Boltzmann Machines … different purposes, eg natural language inference [23], question answering for localized search [24], form driven dialogue systems [25], dialogue management [26], and the …

A comprehensive review on feature set used for anaphora resolution
K Lata, P Singh, K Dutta – Artificial Intelligence Review, 2020 – Springer
In linguistics, the Anaphora Resolution (AR) is the method of identifying the antecedent for anaphora. In simple terms, this is the problem that helps to s.

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 …

Evaluation of Methods for Data-Driven Tools that Empower Mental Health Professionals
OA Demasi – 2019 – escholarship.org
… Novice counselors are unable to assume full responsibility for a crisis situation until they have some experience. Some organizations are considering the use of automated interactive agents (chatbots) to replace human counselors …

Intelligent Decision Support Systems: A Journey to Smarter Healthcare
S Belciug, F Gorunescu – 2020 – Springer
Page 1. Intelligent Systems Reference Library 157 Smaranda Belciug Florin Gorunescu Intelligent Decision Support Systems–A Journey to Smarter Healthcare Page 2. Intelligent Systems Reference Library Volume 157 Series Editors …

Computational Framework for Facilitating Intimate Dyadic Communication
D Utami – 2019 – repository.library.northeastern.edu
… In Chapter 4, I introduced the research platform used throughout the experiments in the dissertation, including the Furhat robot and the IrisTK dialog system. In Chapter 5, I answer the first research question by presenting a study on the acceptance …

LREC 2020 Workshop Language Resources and Evaluation Conference 11–16 May 2020
A Bhatia, S Shaikh – lrec2020.lrec-conf.org
… Goldwasser, 2019). Analysis of chatbot responses (Prakhar Gupta and Bigham, 2019) yields human-judgement correla- tion improvements. Approaches above differ from ours in that they require extensive model training. Our …

Machine Learning in Mental Health
A Thieme, D Belgrave, G Doherty – scss.tcd.ie
Page 1. 1 Machine Learning in Mental Health A Systematic Review of the HCI Literature to Support the Development of Effective and Implementable ML Systems Anja Thieme† Healthcare Intelligence, Microsoft Research, Cambridge, UK, anthie@microsoft.com …

Cognitive Computing Recipes
A Masood, A Hashmi – Springer
Page 1. Cognitive Computing Recipes Artificial Intelligence Solutions Using Microsoft Cognitive Services and TensorFlow — Adnan Masood Adnan Hashmi Foreword by Matt Winkler Page 2. Cognitive Computing Recipes Artificial Intelligence Solutions Using …

Machine learning in mental health: A systematic review of the HCI literature to support the development of effective and implementable ML systems
A Thieme, D Belgrave… – ACM Trans. Comput …, 2020 – designandwellbeing.com
Page 1. 34 Machine Learning in Mental Health: A Systematic Review of the HCI Literature to Support the Development of Effective and Implementable ML Systems ANJA THIEME and DANIELLE BELGRAVE, Microsoft Research GAVIN DOHERTY, Trinity College Dublin …

Machine Learning in Mental Health: A Systematic Review of the HCI Literature to Support the Development of Effective and Implementable ML Systems
A Thieme, D Belgrave, G Doherty – ACM Transactions on Computer …, 2020 – dl.acm.org
Page 1. 34 Machine Learning in Mental Health: A Systematic Review of the HCI Literature to Support the Development of Effective and Implementable ML Systems ANJA THIEME and DANIELLE BELGRAVE, Microsoft Research GAVIN DOHERTY, Trinity College Dublin …

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