Advances in Artificial Intelligence: 23rd Canadian by Dekang Lin (auth.), Atefeh Farzindar, Vlado Kešelj (eds.)

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

This e-book constitutes the refereed court cases of the twenty third convention on synthetic Intelligence, Canadian AI 2010, held in Ottawa, Canada, in May/June 2010. The 22 revised complete papers provided 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 type; textual content summarization and IR; reasoning and e-commerce; probabilistic computer studying; neural networks and swarm optimization; desktop studying and information mining; usual language processing; textual content analytics; reasoning and making plans; e-commerce; semantic net; computing device studying; and information mining.

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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.

A loss function is defined to state how costly each action is, a pair of threshold values on the posterior odds ratio is systematically calculated based on the loss function, and the final decision is to select the action for which the overall cost is minimum. Our experimental results show that the new approach reduces the error rate of classifying a legitimate email to spam, and provides better spam precision and weighted accuracy. Keywords: Spam filter, three-way decision, naive Bayesian classification, Bayesian decision theory, cost.

Unrefined language: some expressions that lack polite manners and the speaker is harsh and rude [12]. Based on the above definitions, when we say flame detection, implicitly we are talking about every context that falls into one or more of the defined cases. Sometime, internet users searching or browsing in some specific sites are frustrated as they encounter offensive, insulting or abusive messages. It occasionally happens even in frequently-used websites like Wikipedia. Therefore an automatic system for discriminating between regular texts and flames would save time and energy during our browsing on the web or in our everyday emails or text messages.

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