Co-occurrence & Dialog Systems 2015


Co-occurrence 

Notes:

In corpus linguistics, collocation refers to the mere co-occurrence of words. Filtering harmful sentences can be done based on three-word co-occurrence. Similarity can be measured based on co-occurrence probabilities for inducing semantic classes. Content may be created from summarized text and keywords extracted from documents using an algorithm based on term co-occurrence. Using a combination of ontology content, structure and co-occurrence information is more beneficial for the extension of large multi-domain ontologies, than using only content, only co-occurrence or only concept denotation information. Automatically-generated summaries and representation of relationships between documents can be accomplished based on the co-occurrence of named entities and on clustering results. Choice of action verbs can be based only on the co-occurrence statistics encoded in a template-based generator for multimodal dialog systems. Statistical models can be based on co-occurrence measurements. Speech recognition errors may be detected based on semantic knowledge, constraint rules and statistical modeling, ie pointwise mutual information and co-occurrence analysis. Semantically different forms of multi-functionality may be represented by the co-occurrence of dialog acts in different types of dialog.

A popular method to estimate co-occurrence is to pose conjunctive queries including both terms to a web search engine, called “co-occurrence in snippets”. One system was designed using co-occurrence between the word in the news article and emotion words. For example, if people express their emotions in text, the single association language feature of a two terms combination, ie “myself ” and “feeling”, has a high frequency of co-occurrence in sentences. Even emoticons can be automatically annotated according to their co-occurrence in a database. In robot navigation, a landmark component can ground novel noun phrases such as “‘the computers” in the perceptual frame of the robot by exploiting object co-occurrence statistics between unknown noun phrases and known perceptual features.

Wikipedia:

See also:

AIML 2010AlchemyAPI 2013 | Apache OpenNLP 2011Apache OpenNLP 2013Apache OpenNLP 2014Automatic Ontology & Dialog SystemsCALO (Cognitive Agent that Learns and Organizes)Chinese & MetaphorCMUSphinx 2011CNN (Convolutional Neural Network) & Natural Language 2014Collocation Extraction & Dialog SystemsConcept SearchConcGramsCorpus WorkbenchCycorp 2011Dialog Act & ChatbotsDIPRE (Dual Iterative Pattern Relation Expansion)Distributional Semantics 2014Hypothesis Generation & Dialog SystemsLanguage Generation ModuleLanguage-independent & Dialog Systems 2011Latent Semantic & Dialog Systems 2012Lexical ChainingLexicogrammar & Natural Language Processing 2014Linear Classifiers & Dialog SystemsLinguistic EmpathyLSA (Latent Semantic Analysis) & Dialog Systems 2014LSM (Latent Semantic Mapping)Machine Learning & ChatbotsMetaphor GenerationN-gram Dialog SystemsNeural Language ModelsNLP Systems EvaluationOntology Alignment & Dialog SystemsOntology Engineering & Dialog SystemsOpenCalais 2014OpenCyc & Natural Language 2013Parse Tree & Dialog Systems 2013Porter StemmerProbabilistic Parser & Dialog SystemsPsychopathology & Natural Language Processing 2014Question Answering Systems & Tourism 2011Question Answering Systems & Tourism 2013Relation Extraction & Dialog SystemsScene Understanding & Natural Language 2013SemEvalSentence ExtractorSnowball 2013Snowball 2014SPARQL & Natural Language Question Answering 2015Stanford CoreNLP & Question Answering 2014Stanford Parser & Dialog SystemsStanford TregexStatistical Semantics 2014Stemming AlgorithmsSVM (Support Vector Machine) & Dialog Systems 2011Text Classification & Dialog Systems 2014Text Segmentation & Dialog SystemsTweetNLPVector Space & Dialog Systems 2014Word-sense Disambiguation & Dialog Systems 2015WordNet & Dialog Systems 2011WordNet & Dialog Systems 2014WordStat 2014XSLT & Dialog Systems


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Evaluation of lexical-based approaches to the semantic similarity of Malay sentences SA Noah, N Omar, AY Amruddin – Journal of Quantitative …, 2015 – Taylor & Francis … [CrossRef] View all references) proposed the ‘pointwise mutual information measure,’ which was based on term co-occurrence counts over large … Their work focused on short sentences which are featured in applications such as conversational agents and dialogue systems. … Related articles All 4 versions

Adequacy–fluency metrics: Evaluating MT in the continuous space model framework RE Banchs, LF D’Haro, H Li – IEEE/ACM Transactions on Audio, …, 2015 – ieeexplore.ieee.org Page 1. 2329-9290 (c) 2015 IEEE. Personal use is permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org/ publications_standards/publications/rights/index.html for more information. This … Cited by 6 Related articles All 3 versions

Speech-Based Personality Assessment T Polzehl – Personality in Speech, 2015 – Springer … communication. Using co-occurrence techniques, the authors observe insufficient correlations. … Lang Speech 38:253–265. Bickmore T, Cassell J (2004) Natural intelligent and effective interaction with multimodal dialogue systems. Kluwer … Related articles

Bringing machine learning and compositional semantics together P Liang, C Potts – Annu. Rev. Linguist., 2015 – annualreviews.org Cited by 13 Related articles All 4 versions

A critical review of recurrent neural networks for sequence learning ZC Lipton, J Berkowitz, C Elkan – arXiv preprint arXiv:1506.00019, 2015 – arxiv.org … convincingly engage in dialogue [Turing, 1950]. Besides dialogue systems, modern interactive systems of economic importance include self-driving cars and robotic surgery, among others. Without an explicit model of sequentiality … Cited by 17 Related articles All 10 versions

Reading Times Predict the Quality of Generated Text Above and Beyond Human Ratings S Zarrieß, S Loth, D Schlangen – ENLG 2015, 2015 – aclweb.org … respect to several criteria. If the NLG component is embedded in an end-to-end sys- tem, such as a dialogue system, 3) extrinsic factors of task success and usefulness of the NLG output can be measured. For corpus-based … Related articles All 8 versions

Semantic similarity from natural language and ontology analysis S Harispe, S Ranwez, S Janaqi… – Synthesis Lectures on …, 2015 – morganclaypool.com … Semantic Role Labeling Martha Palmer, Daniel Gildea, and Nianwen Xue 2010 Spoken Dialogue Systems Kristiina Jokinen and Michael McTear 2009 Introduction to Chinese Natural Language Processing Kam-Fai Wong, Wenjie Li, Ruifeng Xu, and Zheng-sheng Zhang 2009 … Cited by 12 Related articles All 6 versions

The Self-taught Speech Interface B Ons – 2015 – lirias.kuleuven.be … lateral sclerosis CGN Corpus Gesproken Nederlands CVA Cerebrovascular accident DNN Deep Neural Networks GMM Gaussian Mixture Model HMM Hidden Markov Model ICA Independent Component Analysis HAC Histogram of Acoustic Co-occurrence LVCSR large … Related articles

[BOOK] Sentic computing: a common-sense-based framework for concept-level sentiment analysis E Cambria, A Hussain – 2015 – books.google.com … bodily manifestations of affect (facial expressions, posture, behavior, physiology), and affective interfaces and applications (dialogue systems, games, learning … text in which opinions are explicitly expressed such as polarity terms, affect words, and their co-occurrence frequencies … Cited by 42 Related articles All 2 versions

A comparative study of evolving fuzzy grammar and machine learning techniques for text categorization NM Sharef, T Martin, KA Kasmiran, A Mustapha… – Soft Computing, 2015 – Springer … Text categorization can be performed by recognizing the underlying text expression. It has been utilized in many applications, such as dialogue systems, named entity recognition, information retrieval, and text categorization itself. … ( 2010 ) where co-occurrence and WordNet … Related articles All 3 versions

Multi-word unit processing in machine translation J Monti – 2015 – elea.unisa.it … Page 31. 11 shortcomings when translating this particular type of lexical unit. MWUs are not always easy to identify since co- occurrence among the lexemes forming the units may vary a great deal. The most straightforward typology … Related articles All 2 versions

Sentiment analysis: Detecting valence, emotions, and other affectual states from text SM Mohammad – Emotion Measurement, 2015 – books.google.com … Brand management, customer relationship management, and stock market: Sentiment analysis of blogs, tweets, and Facebook posts is already widely used to shape brand image, track customer response, and in developing automatic dialogue systems for handling cus … Cited by 13 Related articles All 4 versions

Spoken Language Understanding in a Nutrition Dialogue System by Mandy B. Korpusik … the lack of a specific nutrient. 1.1 Dialogue Systems Spoken dialogue systems like this one have become increasingly prevalent in today’s … Related articles

Weakly Supervised Natural Language Processing Framework for Abstractive Multi-Document Summarization: Weakly Supervised Abstractive Multi-Document … P Li, W Cai, H Huang – Proceedings of the 24th ACM International on …, 2015 – dl.acm.org Page 1. Weakly Supervised Natural Language Processing Framework for Abstractive Multi-Document Summarization Peng Li Computer Science and Engineering University of Texas at Arlington Arlington, TX, 76019 jerryli1981@gmail.com … Related articles

Novel Methods for Text Preprocessing and Classification T Gasanova – 2015 – deutsche-digitale-bibliothek.de … 3.1 Overview of Spoken Dialogue Systems . … Text preprocessing algorithms usually involve the choice of the feature space (or term set) that is made on the basis of statistical co-occurrence of words, manually created stop-word and ignore-word lists (these lists include words that … Related articles

Learning data-driven models of non-verbal behaviors for building rapport using an intelligent virtual agent R Amini – 2015 – digitalcommons.fiu.edu … The overall goal is to explore the possibilities of using machine learning techniques to move away from hand-crafted rule-based models employed in most of the current health-related dialogue systems (Discussed in Section 2), toward modeling human’s non-verbal behaviors … Cited by 1 Related articles All 2 versions

Content-based Tweets Semantic Clustering and Propagation MA Michalakos – 2015 – repository.ihu.edu.gr … This method treats the collections of documents (tweets in our case) as “bag of words” and assumes that the order of words can be ignored and that the text corpora can be represented by a co-occurrence matrix of words and documents. However this method hides … Related articles

Moving towards the semantic web: enabling new technologies through the semantic annotation of social contents. C Vicient Monllaó – 2015 – tdcat.cesca.es Page 1. MOVING TOWARDS THE SEMANTIC WEB: ENABLING NEW TECHNOLOGIES THROUGH THE SEMANTIC ANNOTATION OF SOCIAL CONTENTS. Carlos Vicient Monllaó Dipòsit Legal: T 155-2015 ADVERTIMENT. … Related articles All 4 versions

Effective use of cross-domain parsing in automatic speech recognition and error detection MA Marin – 2015 – digital.lib.washington.edu … as well as the type, in order to handle them effectively during the subsequent clarification request made by the dialog system component. … Different methods of learning word classes exist; one of the most common involves using word co-occurrence statistics with an agglom- … Cited by 2 Related articles All 2 versions

How a systemic functional grammar works: the RP Fawcett, GH Tucker, YQ Lin – New Concepts in Natural …, 2015 – books.google.com Page 126. Part III Realization 6 How a systemic functional grammar works: the role of realization in realization Robin P. Fawcett, Gordon H. Tucker and Yuen Q. Lin Abstract There is an increasing realization among researchers … Related articles

Robot task planning and explanation in open and uncertain worlds M Hanheide, M Göbelbecker, GS Horn, A Pronobis… – Artificial Intelligence, 2015 – Elsevier A long-standing goal of AI is to enable robots to plan in the face of uncertain and incomplete information, and to handle task failure intelligently. This paper. Cited by 13 Related articles All 3 versions

Unsupervised extraction of semantic relations using discourse information J Conrath – 2015 – thesesups.ups-tlse.fr … By targeting a specific set of relations, we avoid the difficulties that beset distributional semantics with formulating entailments in a proper way. More specifically, our approach is based on the co-occurrence between pairs of predicates and discourse connectives. … Related articles

Sentic Computing E Cambria, A Hussain – Cognitive Computation, 2015 – Springer … bodily manifestations of affect (facial expressions, posture, behavior, physiology), and affective interfaces and applications (dialogue systems, games, learning … text in which opinions are explicitly expressed such as polarity terms, affect words, and their co-occurrence frequencies … Cited by 2 Related articles All 8 versions

RoboCup @Home: Analysis and results of evolving competitions for domestic and service robots L Iocchi, D Holz, J Ruiz-del-Solar, K Sugiura… – Artificial Intelligence, 2015 – Elsevier … papers. Competitions related to speech processing and dialogue systems. In the … task. The Loebner Prize, 9 which is an instantiation of the Turing Test, can be categorized into a competition on (text-based) dialogue systems. It started … Cited by 4 Related articles All 5 versions

Experiments in Information Retrieval KCÓ Kane – pdfs.semanticscholar.org … 11.1 Building a Term-Term Co-Occurrence Matrix…..101 11.2 Term-Term-Term Connection Matrix…..108 11.3 Construction of Term Phrases…..108 11.3.1 Term Cohesion … Related articles All 4 versions

Structural information aware deep semi-supervised recurrent neural network for sentiment analysis W Rong, B Peng, Y Ouyang, C Li, Z Xiong – Frontiers of Computer Science, 2015 – Springer … Obviously, the basis of lexicon based approaches is the generation of sentiment lexicon corpus. In some work, the polarity labels of terms are determined by a set of pre-defined seed words with the consideration of their co-occurrence re- lationship [26]. … Cited by 2 Related articles All 4 versions

[BOOK] Computational Linguistics and Intelligent Text Processing: 16th International Conference, CICLing 2015, Cairo, Egypt, April 14-20, 2015, Proceedings A Gelbukh – 2015 – books.google.com … 335 Alia El Bolock and Slim Abdennadher A Multi-strategy Approach for Lexicalizing Linked Open Data….. 348 Rivindu Perera and Parma Nand A Dialogue System for Telugu, a Resource-Poor Language….. 364 Mullapudi Ch. … Related articles All 2 versions

Spoken Term Detection and Spoken Word Sense Induction on Noisy Data J Chiu – 2015 – cs.cmu.edu … 11 2.1.1 Word Recurrence in Dialogue Systems . . . . . … 2.1.1 Word Recurrence in Dialogue Systems (Barnett, 1973) propose the “Thematic Memory” as the content-word equivalent of the user-state syntax model. … Related articles

Natural Language Processing for Social Media A Farzindar, D Inkpen – Synthesis Lectures on Human …, 2015 – morganclaypool.com … Semantic Role Labeling Martha Palmer, Daniel Gildea, and Nianwen Xue 2010 Spoken Dialogue Systems Kristiina Jokinen and Michael McTear 2009 Introduction to Chinese Natural Language Processing Kam-Fai Wong, Wenjie Li, Ruifeng Xu, and Zheng-sheng Zhang 2009 … Cited by 4 Related articles All 5 versions

Chinese spelling checker based on an inverted index list with a rescoring mechanism JF Yeh, WY Chen, MC Su – ACM Transactions on Asian and Low- …, 2015 – dl.acm.org Page 1. i i i i 17 Chinese Spelling Checker Based on an Inverted Index List with a Rescoring Mechanism JUI-FENG YEH, WEN-YI CHEN, and MAO-CHUAN SU, National Chiayi University An approach is proposed for Chinese … Cited by 1 Related articles

All together now… This time with meaning: A hierarchical lexicon for semantic coordination B Noble – 2015 – illc.uva.nl … 8 Page 9. 1.2. Contributions Applied Formal Semantics A final motivation for this thesis is a consideration for its possible applications both in dialogue systems and in data-driven semantics. The possible applications to dialogue … Related articles All 3 versions

[BOOK] Advances in Artificial Intelligence and Its Applications: 14th Mexican International Conference on Artificial Intelligence, MICAI 2015, Cuernavaca, Morelos, … OP Lagunas, OH Alcántara, GA Figueroa – 2015 – books.google.com Page 1. Obdulia Pichardo Lagunas Oscar Herrera Alcántara Gustavo Arroyo Figueroa (Eds.) Advances in Artificial Intelligence and Its Applications 14th Mexican International Conference on Artificial Intelligence, MICAI 2015 … Related articles

[BOOK] Language Identification Using Excitation Source Features KS Rao, D Nandi – 2015 – Springer … Some of the topics covered in this series include the presentation of real life commercial deployment of spoken dialog systems, contemporary methods of speech parameterization, developments in information security for automated speech, forensic speaker recognition, use … Related articles All 6 versions

[BOOK] Computer Interpretation of Metaphoric Phrases SW Russell – 2015 – books.google.com Page 1. Sylvia Weber Russell Computer Interpretation of Metaphoric Phrases Page 2. Also of interest Natural Language Processing and Cognitive Science: Proceedings 2014 Sharp, Delmonte (Eds), 2015 ISBN 978-1-5015-1042 … Related articles All 4 versions

[BOOK] Personality in Speech T Polzehl – 2015 – Springer Page 1. T-Labs Series in Telecommunication Services Tim Polzehl Personality in Speech Assessment and Automatic Classification Page 2. T-Labs Series in Telecommunication Services Series editors Sebastian Möller, Berlin … Cited by 4 Related articles All 6 versions

[BOOK] Biometric and intelligent decision making support A Kaklauskas – 2015 – Springer Page 1. Intelligent Systems Reference Library 81 Arturas Kaklauskas Biometric and Intelligent Decision Making Support Page 2. Intelligent Systems Reference Library Volume 81 Series editors Janusz Kacprzyk, Polish Academy … Cited by 5 Related articles All 4 versions

Stuck and frustrated or in flow and happy: Sensing developers’ emotions and progress SC Müller, T Fritz – Proceedings of the 37th International Conference on …, 2015 – dl.acm.org … That’s annoying.” (S13) This co-occurrence of comments on emotions and on progress in participants’ answers further supports our findings from RQ1, indicating a correlation between a developer’s positive and negative emotions and the perceived progress. … Cited by 13 Related articles All 2 versions

Deep learning approaches to problems in speech recognition, computational chemistry, and natural language text processing GE Dahl – 2015 – tspace.library.utoronto.ca Page 1. Deep learning approaches to problems in speech recognition, computational chemistry, and natural language text processing by George Edward Dahl A thesis submitted in conformity with the requirements for the degree … Cited by 1 Related articles All 5 versions