Learning Classifier & Dialog Systems


Learning Classifier System (LCS) is a rule-based machine learning paradigm that combines a discovery component, such as a genetic algorithm, with a learning component that performs supervised learning, reinforcement learning or unsupervised learning. The discovery component is responsible for generating a set of rules, while the learning component is responsible for adjusting the parameters of these rules based on the data.

One of the main advantages of LCS is that it can learn from both labeled and unlabeled data, which makes them well suited for problems with limited labeled data. LCS can also adapt to changing environments, through the use of a reinforcement learning component that updates the system’s parameters based on the feedback from the environment.

LCS have been used in a variety of applications, such as medical diagnosis, financial prediction, and robotics. They have also been used in dynamic and non-stationary environments, such as game playing and control systems, which makes it effective for real-world problems.

However, LCS can also be limited in terms of computational resources and the size of data sets they can handle. Additionally, LCS are not good at generalization, which means that they tend to over-fit the training data and they are often less accurate than other rule-based or non-rule-based approaches.

The references below discuss the use of machine learning techniques, specifically learning classifier systems, in the context of natural language processing and dialogue systems. It mentions the challenges of using sentiment analysis in informal communications and the potential for a domain-independent knowledge base to be used as a source of world knowledge for various language technology applications. The text also mentions that such a model has a wide range of potential applications, such as in the generation of more natural-sounding spoken dialog systems and as a component in an intelligent tutoring system. Additionally, the text highlights the application of learning classifier systems in various domains such as robotics, computer games, network routing, and natural language dialogue systems.


See also:

CLASSiC (Computational Learning in Adaptive Systems for Spoken Conversation) | Linear Classifiers & Dialog Systems | Machine Learning | Machine Learning & ChatbotsMachine Learning & Dialog Systems | Rule Learning & Dialog Systems | Statistical Classification & Dialog SystemsTiMBL (Tilburg Memory Based Learner) & Dialog Systems | Weka & Dialog Systems

[PDF] Sentiment analysis of informal textual communication in cyberspace G Paltoglou, S Gobron, M Skowron, M Thelwall… – Proc. Engage, 2010 – osgk.ac.at … constitute of short, informal, textual exchanges, it is essential that the sentiment analysis component integrated in the dialog system is able … no clear “golden standard” exists in the domain of informal communications with which to train a machine-learning classifier in opposition … Cited by 20 Related articles All 10 versions

[PDF] from rodneynielsen.com Recognizing entailment in intelligent tutoring systems RD Nielsen, W Ward, JH Martin – Natural Language …, 2009 – Cambridge Univ Press … Finally, we train a machine learning classifier on the training data and use it to classify the unseen examples in the test set, assigning one of the five Tutor-Labels separately for each reference answer facet to indicate the student’s understanding of that facet. … Cited by 16 Related articles All 16 versions

Method and apparatus for providing proper or partial proper name recognition F Weng, L Zhao – US Patent 7,865,356, 2011 – Google Patents … learning classifier. In this regard, the accuracy of proper name recognition may be increased and tagging errors may be reduced. An exemplary embodiment and/or exemplary method or apparatus of the present invention may be applied, for example, in spoken dialog systems … Related articles All 5 versions

[PDF] from pitt.edu [PDF] Classifying turn-level uncertainty using word-level prosody D Litman, M Rotaru, G Nicholas – Proceedings of Interspeech, 2009 – cs.pitt.edu … Our corpus consists of 9588 user turns from 347 spoken dia- logues between 80 users and ITSPOKE (Intelligent Tutoring SPOKEn dialogue system) [11], a speech … The machine learning classifier is trained on this data to produce a word-level classifier (ie we predict word labels … Cited by 9 Related articles All 11 versions

[PDF] from yum2.net Extracting social meaning: Identifying interactional style in spoken conversation D Jurafsky, R Ranganath, D McFarland – Proceedings of Human …, 2009 – dl.acm.org … Automatically extracting social meaning and intention from spoken dialogue is an impor- tant task for dialogue systems and social com- puting … Our goal is to detect three of the style variables, in particular awkward, friendly, or flirtatious speakers, via a machine learning classifier. … Cited by 23 Related articles All 30 versions

[PDF] from rochester.edu Extracting events and temporal expressions from text N UzZaman, JF Allen – Semantic Computing (ICSC), 2010 IEEE …, 2010 – ieeexplore.ieee.org … of documents and apply the temporal structure in other applications like textual entailment, question answering, dialog systems or others. … Temporal Expression Our temporal expression extraction module is a hybrid between traditional machine learning classifier and the TRIPS … Cited by 4 Related articles All 16 versions

[PDF] from ua.ac.be 6 Memory-Based Learning W Daelemans – The Handbook of Computational Linguistics and …, 2010 – books.google.com … Applications As explained in Section 1, MBL shares its generic applicability to classi?cation tasks with any other machine learning classi?er. … history features (eg, pre- vious dialogue acts), and acoustic features of recognized speech in the context of spoken dialogue systems. … Related articles All 14 versions

[PDF] from aq.gs [PDF] Crowdflow: Integrating machine learning with mechanical turk for speed-cost-quality flexibility AJ Quinn, BB Bederson, T Yeh, J Lin – Better performance over iterations, 2010 – aq.gs … Machine: An abstract class representing a generic machine learning classifier with train(question,answer), evaluate(question), init(), and terminate() methods. … [6] Horvitz, E., Paek, T.. Complementary computing: policies for transferring callers from dialog systems to human … Cited by 9 Related articles All 17 versions

[PDF] from upc.edu Semantic services in freeling 2.1: Wordnet and ukb L Padró, S Reese, E Agirre, A Soroa – 2010 – upcommons.upc.edu … are needed for most natural language processing (NLP) applica- tions such as Machine Translation, Summariza- tion, Dialogue systems, Text mining, etc … Uses TCO and hypernym relations be- tween two mentions as features used by a ma- chine learning classifier to determine … Cited by 5 Related articles All 8 versions

[PDF] from aclweb.org From annotator agreement to noise models B Beigman Klebanov, E Beigman – Computational Linguistics, 2009 – dl.acm.org … They showed that a machine-learning classifier is sensitive to the type of noise in the data. … 2006. Characterizing and predicting corrections in spoken dialogue systems. Computational Linguistics, 32(3):417–438. Markert, Katja and Malvina Nissim. 2002. … Cited by 12 Related articles All 16 versions

[PDF] from unt.edu Annotating and identifying emotions in text C Strapparava, R Mihalcea – Intelligent Information Access, 2010 – Springer Page 1. Annotating and Identifying Emotions in Text Carlo Strapparava and Rada Mihalcea Abstract. This paper focuses on the classification of emotions and polarity in news headlines and it is meant as an exploration of the connection between emotions and lexical semantics. … Cited by 7 Related articles All 3 versions

[PDF] from dfki.de [PDF] Determining latency for on-line dialog act classification S Germesin, T Becker, P Poller – Poster Session for the 5th International …, 2008 – ami.dfki.de … The machine learning classifier is implemented with the help of the freely avail- able WEKA toolkit [7] which contains many state-of-the-art … dialogue act tagset.”, In Workshop notes of the Fourth IJCAI Workshop on Knowledge and Reasoning in Pracical Dialogue Systems, 2005 2 … Cited by 7 Related articles All 3 versions

Feature selection on multi-physiological signals for emotion recognition BJ Park, EH Jang, SH Kim, C Huh… – … and Industries (ICEI), …, 2011 – ieeexplore.ieee.org … In addition that, we have introduced an instance-based learning classifier with feature selection learned by genetic algorithms (GAs … and J. Williams, “Emotion Recognition Using Bio-Sensors: First Step Towards an Automatic System,” Affective Dialogue Systems, Tutorial and … Related articles All 12 versions

[PDF] from aaaipress.org Analyzing dialog coherence using transition patterns in lexical and semantic features A Purandare, D Litman – Proceedings 21st International FLAIRS …, 2008 – aaai.org … We build a machine learning classifier using local transition pat- terns that span over adjacent dialog turns and encode lexical as well as … Coherence is also important when it comes to speech and dialog based applications, so that a dialog system is able to make coherent … Cited by 4 Related articles All 10 versions

[PDF] from rochester.edu Event and temporal expression extraction from raw text: first step towards a temporally aware system N UzZaman, FA JAMES – International Journal of Semantic …, 2010 – World Scientific … work. 6. Temporal Expression Extraction 6.1. Recognizing temporal expression Our temporal expression extraction module is a hybrid between a traditional machine learning classifier and the TRIPS parser extractor. For the … Cited by 7 Related articles All 15 versions

[BOOK] KI 2009: Advances in Artificial Intelligence: 32nd Annual German Conference on AI, Paderborn, Germany, September 15-18, 2009, Proceedings B Mertsching, M Hund, Z Aziz – 2009 – books.google.com … Page 15. Table of Contents XV Controlling a Four Degree of Freedom Arm in 3D Using the XCSF Learning Classi?er System….. … 201 Hauke Tonnies Natural Language Processing Semi-automatic Creation of Resources for Spoken Dialog Systems….. … All 3 versions

[PDF] from atala.org [PDF] A SVM cascade for agreement/disagreement classifcation P Andrews, S Manandhar – Journal du Traitement Automatique des …, 2009 – atala.org … In fact, in current dialogue systems, shallow understanding of the user utterances is preferred. … Hillard et al. (2003) proposed a first step towards a statistical method for agreement/disagreement classification by developing a supervised learning classifier based on an annotated … Cited by 1 Related articles All 2 versions

[BOOK] Reinforcement Learning: State-of-the-Art M Wiering, M van Otterlo – 2012 – books.google.com … for a long time. Application domains range from robotics and computer games to network routing and natural language dialogue systems and reinforcement learning papers appear at fora dealing with these topics. A large por … Cited by 4 Related articles

[PDF] from fcla.edu [PDF] Using student mood and task performance to train classifier algorithms to select effective coaching strategies within Intelligent Tutoring Systems (ITS) RA Sottilare – 2009 – purl.fcla.edu … choose the optimal strategy. A set of production rules were also developed to train a machine learning classifier using Witten & Frank’s (2005) Waikato Environment for Knowledge Analysis (WEKA) toolset. The classifier was tested to determine its ability to recognize critical … Cited by 1 Related articles All 3 versions

[BOOK] Modern Approaches in Applied Intelligence: 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, … KG Mehrotra, CK Mohan, JC Oh, PK Varshney, M Ali – 2011 – books.google.com … The papers in the proceedings cover a wide number of topics including feature extraction, discretization, clustering, classification, diagnosis, data refinement, neural networks, genetic algorithms, learning classifier systems, Bayesian and probabilistic methods, image processing … Related articles All 6 versions

[PDF] from upenn.edu Evolving optimal inspectable strategies for spoken dialogue systems D Toney, J Moore, O Lemon – … of the NAACL, Companion Volume: Short …, 2006 – dl.acm.org … Reasoning in Practical Dialogue Systems, Edin- burgh, UK, July. John Holland. 1976. Adaptation. In Rosen R. and F. Snell, editors, Progress in theoretical biology. Plenum, New York. Tim Kovacs. 2000. Strength or accuracy? Fitness cal- culation in learning classifier systems. … Cited by 5 Related articles All 26 versions

[PDF] from mit.edu [PDF] Reducing recognition error rate based on context relationships among dialogue turns HC Wu, S Seneff – Proc. Interspeech, 2007 – sls.csail.mit.edu … The goal is to both provide a confidence score and to reduce recognition error, in a dialogue system in- … In Section 5 we describe the Learning Classifier System (LCS) model and our decisions about parameterizing the mod- els. … Cited by 3 Related articles All 11 versions

Solving relational and first-order logical markov decision processes: A survey M Otterlo – Reinforcement Learning, 2012 – Springer Page 1. Chapter 8 Solving Relational and First-Order Logical Markov Decision Processes: A Survey Martijn van Otterlo Abstract. In this chapter we survey representations and techniques for Markov de- cision processes, reinforcement … Cited by 3 Related articles All 2 versions

[PDF] from ets.org [PDF] Identifying Speech Acts in E-Mails: Toward Automated Scoring of the TOEIC® E-Mail Task R De Felice, P Deane – 2012 – ets.org … feature vector for each utterance. We train a machine learning classifier on the vectors to … Therefore, for the purpose of designing a set of features to be used in a machine learning classifier, it seems appropriate to take into account the insights … Related articles

When the answer comes into question in question-answering: survey and open issues AC Mendes, L COHEUR – Natural Language Engineering, 2012 – Cambridge Univ Press Page 1. Natural Language Engineering: page 1 of 32. c Cambridge University Press 2012 doi:10.1017/S1351324911000350 1 When the answer comes into question in question-answering: survey and open issues ANA CRISTINA … Cited by 1 Related articles All 2 versions

[PDF] from ed.ac.uk Evolutionary reinforcement learning of spoken dialogue strategies D Toney – 2007 – lac-repo-live7.is.ed.ac.uk … end-to-end spoken dialogue system. The dialogue system allowed real users to make flight enquiries using a live database for an Edinburgh-based airline. The performance of the learned and hand-coded strategies were compared. … 41 3.3 Learning Classifier Systems . . . . . … Cited by 4 Related articles All 9 versions

[PDF] from ijs.si [PDF] Towards combining finite-state, ontologies, and data driven approaches to dialogue management for multimodal question answering D Sonntag – Proceedings of the 5th Slovenian First International …, 2006 – nl.ijs.si … which path in the undeterministic FSA to follow, into a classification problem to be solved by any suitable supervised machine learning classifier. … resemble the features used in previous experiments to classify user mod- els (Komatani et al., 2003) for dialogue system adaptivity. … Cited by 7 Related articles All 6 versions

[PDF] from rodneynielsen.com [BOOK] Learner answer assessment in intelligent tutoring systems RD Nielsen – 2007 – books.google.com Page 1. LEARNER ANSWER ASSESSMENT IN INTELLIGENT TUTORING SYSTEMS by RODNEY D. NIELSEN MS, University of Colorado, Boulder, 2005 A thesis submitted to the Faculty of the Graduate School of the University … Cited by 1 Related articles All 7 versions

[PDF] from aclweb.org Modeling Regular Polysemy: A Study on the Semantic Classification of Catalan Adjectives G Boleda, S Schulte im Walde, T Badia – Computational Linguistics, 2012 – MIT Press … this article. The semantic properties of adjectives can also be exploited in advanced NLP tasks and applications such as Question Answering, Dialog Systems, Natural Language Generation, or Information Extraction. For instance … Cited by 1 Related articles All 8 versions

[PDF] from uvt.nl [PDF] Memory-based understanding of user utterances in a spoken dialogue system: Effects of feature selection and co-learning A Van den Bosch – Workshop Proceedings of the 6th International …, 2005 – arno.uvt.nl … The research material on which this study is based is collected from interactions with the OVIS dialogue system [8]. OVIS, for ‘Openbaar Vervoer Informatie Systeem’ (Public Transport Information … We use a memory-based learner mbl as the machine-learning classifier of choice. … Cited by 3 Related articles All 6 versions

[PDF] from bham.ac.uk [PDF] Developing conversational interfaces with XCS D Toney, J Moore, O Lemon – … Workshop on Learning Classifier …, 2006 – cs.bham.ac.uk … Discourse. General Terms Algorithms, Design, Experimentation, Performance. Keywords Learning Classifier Systems, XCS, Conversational Interfaces, Spoken Dialogue Systems, Conversational Strategies. 1. INTRODUCTION … Cited by 2 Related articles All 3 versions

[PDF] from pitt.edu [PDF] Prosodic feature generation for back-channel prediction T Solorio, O Fuentes, N Ward, Y Al Bayyari – Proceedings of Interspeech, 2006 – cs.pitt.edu … Such a model has a wide range of potential applications, for example in the gen- eration of more natural-sounding spoken dialog systems and as a component in an intelligent tutoring system for students of foreign … These features are then input to a machine learning classifier. … Cited by 3 Related articles All 7 versions

[PDF] from dfki.de Fusion and coordination for multimodal interactive information presentation H Bunt, M Kipp, M Maybury… – Multimodal intelligent …, 2005 – books.google.com … t spccmc analysis’ gesture parser speech gaze machine learning classifier J Svlieme Framework 1 Scheme Layer Framework Layer r visualization Coding Tool (API) general analvsis ‘1 Application Layer Logical Layer Annotation Framework (Tracks, types, objects etc.) Figure 6 … Cited by 25 Related articles All 11 versions

[PDF] from uni-osnabrueck.de [PDF] Learning the semantics of Wikipedia hyperlinks D Bauer – 2007 – cogsci.uni-osnabrueck.de … But many language technology appli- cations could profit from a domain independent knowledge base as a source of world knowledge (eg dialog systems and question answering systems, but also text generation, summarization and machine translation). … Cited by 1 Related articles All 17 versions

[PDF] from aclweb.org Semantic role labeling M Palmer, D Gildea, N Xue – Synthesis Lectures on Human …, 2010 – morganclaypool.com … Semantic Role Labeling Martha Palmer, Daniel Gildea, and Nianwen Xue 2010 Spoken Dialogue Systems Kristiina Jokinen and Michael McTear 2010 Introduction to Chinese Natural Language Processing Kam-Fai Wong, Wenji Li, Ruifeng Xu, and Zheng-sheng Zhang 2009 … Cited by 23 Related articles All 13 versions

[PDF] from uwm.edu Question Classification in the Cancer Domain AY Kurmally – 2012 – dc.uwm.edu … Finally, we will discuss the next steps in continuing this research to construct a functional QA dialog system for cancer questions. Page 14. 6 … 3.2.3 Manual Classification In order to use a corpus to train a Supervised Machine Learning classifier, items …

[BOOK] MedInfo 2007 KA Kuhn, JR Warren, TY Leong – 2007 – books.google.com … A. Hamm, Sarah E. Knoop, Peter Schwarz, Aaron D. Block and Warren L. Davis IV Semantic Issues in Healthdata Classification A New Machine Learning Classifier for High … A New Architecture for Health-Care Dialogue Systems 1063 LM Rojas-Barahona and T. Giorgino … Cited by 3 Related articles All 2 versions

[PDF] from antona.sk [PDF] Novel approaches to acquisition and maintenance of user model A Andrejko – Inf. Sciences and Tech. Bulletin of the ACM …, 2009 – publications.antona.sk Page 1. Anton Andrejko Novel Approaches to Acquisition and Maintenance of User Model Dissertation thesis Slovak University of Technology Bratislava, Slovakia Page 2. Page 3. Anton Andrejko Novel Approaches to Acquisition and Maintenance of User Model … Cited by 3 Related articles All 11 versions

Emotion detection device & method for use in distributed systems IM Bennett – US Patent App. 11/294,918, 2005 – Google Patents Page 1. US 20060122834A1 (i9) United States (12) Patent Application Publication oo) Pub. No.: US 2006/0122834 Al Bennett (43) Pub. Date: Jun. 8,2006 (54) EMOTION DETECTION DEVICE & METHOD FOR USE IN DISTRIBUTED … All 2 versions

[PDF] from wseas.us [PDF] Context-free grammar induction using evolutionary methods. O Unold – WSEAS Transactions on Circuits and Systems, 2003 – wseas.us … This paper investigates the use of evolutionary methods: a genetic algorithm, a genetic programming and learning classifier systems for inferring CFG based parser. … 185-193. [34] O. Unold, A Fuzzy Automaton Approach to Dialog Systems, Proc. … Cited by 7 Related articles All 2 versions

Learning, logic, and probability: A unified view P Domingos – Lecture Notes in Computer Science, 2004 – books.google.com … XXV, 1208 pages. 2004. Vol. 3068: E. Andre, L. Dybkjser, W. Minker, P. Heis- terkamp (Eds.), Affective Dialogue Systems. XII, 324 pages. 2004. Vol. … 2003. Vol. 2661: PL. Lanzi, W. Stolzmann, SW Wilson (Eds.), Learning Classifier Systems. VII, 231 pages. 2003. Vol. … Cited by 1 Related articles BL Direct All 4 versions

[PDF] from psu.edu [PDF] GRAEL: an agent-based evolutionary computing approach for natural language grammar development G De Pauw – INTERNATIONAL JOINT CONFERENCE ON …, 2003 – Citeseer … was conducted on the small, homogeneous ATlS-corpus, which consists of a collection of annotated sentences recorded by a spoken- dialogue system. … set of examples but as raw material that needs to be pre-processed before it can be used by a machine learning classifier. … Cited by 2 Related articles BL Direct All 14 versions

???????????????????? ???, ??? – ?????, 2007 – airitilibrary.com … Recently research on developing spoken dialogue systems to provide conversational practice for a learner of a foreign language has been conducted. … In this paper a Learning Classifier System technique is presented to assist the process of selection from a list of N-best … All 2 versions

[PDF] from tudelft.nl [PDF] Topics in speech recognition DALK Fa – 2006 – kbs.twi.tudelft.nl … For certain spoken telephone dialog systems it is desirable that these English names are properly recognized. … When enough data and a good algorithm is used, the machine learning classifier is capable of successfully classifying unseen data. … Related articles All 2 versions

[BOOK] New directions in intelligent interactive multimedia GA Tsihrintzis, M Virvou – 2008 – books.google.com … Jan Drugowitsch Design and Analysis of Learning Classi?er Systems, 2008 ISBN 978-3-540-79865-1 Vol. … Perception Chip Using ; Constraint ‘, Park, Hong Jeong 565 DerceptuaHy-Inspired Methods for Blob Extraction Icoz 577 : A Multimodal Dialogue System … Cited by 2 Related articles All 5 versions

ASR 253,277,301,341 ATIS 461 audio collection 347 audio-visual speech recognition 205 Austrian 26,341 AB MH – Text, Speech and Dialogue: 5th International …, 2002 – books.google.com … based 115 -trigram-based 20 tagging -PoS 3, 19115132 -sense 158 Taiwan corpus 119 TD-PSOLA 237 TEA text analyzer 99 telemedicine system 449 telephone dialogue system 384 telephony system … W. Stolzmann, SW Wilson (Eds.), Advances in Learning Classifier Systems. …

[PDF] from psu.edu [PDF] Recent Developments in Human Language Technology W Winiwarter – 2002 – Citeseer … specifications in natural language, multimodal search engines for news messages, embedded adaptive machine translation environments, multilingual terminologies and ontologies for the Semantic Web, and speech and multimodal dialogue systems for telephony applications. … Related articles All 10 versions

[PDF] from sics.se [BOOK] Towards socio-emotionally rich interactive narratives J Laaksolahti – 2003 – sics.se Page 1. Towards Socio-Emotionally Rich Interactive Narrative Jarmo Laaksolahti 17th May 2003 Page 2. ABSTRACT socio-emotionally rich interactive narratives are a form of digital entertainment that focuses on immersion in a story. … Cited by 2 Related articles All 5 versions