By Atefeh Farzindar, Vlado Keselj
This publication constitutes the refereed lawsuits of the twenty third convention on synthetic 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 displays have been rigorously reviewed and chosen from ninety submissions. The papers are equipped in topical sections on textual content class; textual content summarization and IR; reasoning and e-commerce; probabilistic computing device studying; neural networks and swarm optimization; computer studying and information mining; normal language processing; textual content analytics; reasoning and making plans; e-commerce; semantic internet; desktop studying; and knowledge mining.
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Extra info for Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 - June 2, 2010,
Cognition and Emotion 6, 169–200 (1992) 3. : Identifying Expressions of Emotion in Text. Master’s thesis, University of Ottawa, Ottawa, Canada (2007) 4. : Compositionality Principle in Recognition of Fine-Grained Emotions from Text. In: Proc. Third International ICWSM Conference, pp. 278–281 (2009) 5. : Emotions from text: machine learning for text- based emotion prediction. In: Proc. Joint Conference on Human Language Technology / Empirical Methods in Natural Language Processing (HLT/EMNLP 2005), Vancouver, Canada, pp.
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.
38 By comparing the results of the second level of gold standard classification with the flat classification, we can see that the F-measure of all the emotional classes in the two-level experiment is higher than the F-measure of the emotional classes in the flat classification. In two emotion classes, however, the precision of the flat approach is higher. In disgust the difference is insignificant but the difference between precision and recall of the happiness class in the flat approach and the two-level approach cannot be ignored.
Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 - June 2, 2010, by Atefeh Farzindar, Vlado Keselj