Advances in Artificial Intelligence: 23rd Canadian by Atefeh Farzindar, Vlado Keselj

By Atefeh Farzindar, Vlado Keselj

This publication constitutes the refereed lawsuits of the twenty third convention on man made Intelligence, Canadian AI 2010, held in Ottawa, Canada, in May/June 2010. The 22 revised complete papers offered including 26 revised brief papers, 12 papers from the graduate scholar symposium and the abstracts of three keynote shows have been conscientiously reviewed and chosen from ninety submissions. The papers are geared up in topical sections on textual content category; textual content summarization and IR; reasoning and e-commerce; probabilistic computing device studying; neural networks and swarm optimization; computing device studying and information mining; average language processing; textual content analytics; reasoning and making plans; e-commerce; semantic internet; laptop studying; and information mining.

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Classifying news stories using memory based reasoning. In: Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 59–65 (1992) 10. html 11. : Machine Learning. McGraw-Hill, New York (1997) 12. : The threshold approach to clinical decision making. New England Journal of Medicine (1980) 13. : Rough membership functions. , Kacprzyk, J. ) Advances in the Dempster-Shafer Theory of Evidence, pp. 251–271. John Wiley and Sons, New York (1994) 14.

Sl¸ vol. 4481, pp. 1–12. Springer, Heidelberg (2007) 18. : An email classification scheme based on decision-theoretic rough set theory and analysis of email security. In: Proceeding of 2005 IEEE Region 10 TENCON, pp. 1–6 (2005) 19. : Variable precision rough sets model. ca Abstract. We explore the task of automatic classification of texts by the emotions expressed. We consider how the presence of neutral instances affects the performance of distinguishing between emotions. Another facet of the evaluation concerns the relation between polarity and emotions.

We discuss future work and present a few conclusions in Section 5. 2 Previous Work Computational approaches to emotional analysis have focused on various emotion modalities, but only limited work has been done in the direction of automatic recognition of emotion in text [3]. In SemEval 2007, one of the tasks was carried out in an unsupervised setting and the emphasis was on the study of emotion in lexical semantics [7-10]. The participants in the SemEval 2007 workshop took a linguistic approach, using enriched lexical resources such as SentiWordNet and WordNetAffect [8].

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