Download e-book for iPad: Advances in Artificial Intelligence: 23rd Canadian by Dekang Lin (auth.), Atefeh Farzindar, Vlado Kešelj (eds.)

By Dekang Lin (auth.), Atefeh Farzindar, Vlado Kešelj (eds.)

ISBN-10: 3642130593

ISBN-13: 9783642130595

This e-book 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 pupil 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 category; textual content summarization and IR; reasoning and e-commerce; probabilistic desktop studying; neural networks and swarm optimization; desktop studying and information mining; traditional language processing; textual content analytics; reasoning and making plans; e-commerce; semantic net; desktop studying; and information mining.

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Additional resources for Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings

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Were kept. Punctuations (including “ ”) could be useful for determining the scope of speaker’s messages. This step prevents the system from coming up with a lot of useless tokens as features for our first-level classifier. Offensive Language Detection Using Multi-level Classification 21 probability of being a flame for each context. At this level we make a pattern for each of the entries that match a variety of word sequences (Replacing Somebody or Something wild cards for the above example).

Learning Subjective Language. Computational Linguistics 30(3), 277–308 (2004) 4. : Integrating Knowledge for Subjectivity Sense Labeling. In: Joint Conference of the North American Chapter of the Association for Computational Linguistics and the Human Language Technologies Conference, NAACLHLT 2009 (2009) 5. : Annotating expressions of opinions and emotions in language. Language Resources and Evaluation 39(2-3), 165–210 (2005) 6. : Combining Naive Bayes and Decision Tables. In: FLAIRS Conference, pp.

Flattened confusion matrices for all 6 classification results – True Pos. shows the number of texts which correctly classified as Okay; False Pos. shows the number of texts which falsely classified as Okay; True Neg. shows the number of texts which correctly classified as Flame and the False Neg. shows the number of texts which falsely classified as Flame True Pos. False Pos. True. Neg. False Neg. Classification# 936 102 349 138 1 89 16 36 11 2 999 39 385 102 3 84 3 27 8 4 1022 16 454 33 5 86 0 32 4 6 At the second classification level, we used the most expressive selected features (~1700 features selected by classification); the results of the Naïve Bayes Multinomial Updateable Classifier, applied with 10-folds cross-validation are shown 4 As most parts of the computation are run prior to the final detection, the system could be applied easily in online interactive applications.

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Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings by Dekang Lin (auth.), Atefeh Farzindar, Vlado Kešelj (eds.)


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