By Atefeh Farzindar, Vlado Keselj
This booklet 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 awarded including 26 revised brief papers, 12 papers from the graduate pupil symposium and the abstracts of three keynote shows have been rigorously 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 desktop studying; neural networks and swarm optimization; computing device studying and information mining; normal language processing; textual content analytics; reasoning and making plans; e-commerce; semantic net; laptop studying; and knowledge mining.
Read or Download Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 - June 2, 2010, PDF
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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,
The overall risk R is the expected loss associated with a given decision rule. Since R(τ (x)|x) is the conditional risk associated with action τ (x), the overall risk is deﬁned by: R= R(τ (x)|x)P r(x), (7) x where the summation is over the set of all possible descriptions of emails. If τ (x) is chosen so that R(τ (x)|x) is as small as possible for every x, the overall risk R is minimized. Thus, the optimal Bayesian decision procedure can be formally stated as follows. For every x, compute the conditional risk R(ai |x) for i = 1, .
Yao, and J. Luo The Naive Bayesian Spam Filtering The naive Bayesian spam ﬁltering is a probabilistic classiﬁcation technique of email ﬁltering . It is based on Bayes’ theorem with naive (strong) independence assumptions [6,11,14]. , xn are the values of attributes of emails. Let C denote the legitimate class, and C c denote the spam class. Based on Bayes’ theorem and the theorem of total probability, given the vector of an email, the conditional probability that this email is in the legitimate class is: P r(C|x) = P r(C)P r(x|C) , P r(x) (1) where P r(x) = P r(x|C)P r(C) + P r(x|C c )P r(C c ).
Another facet of the evaluation concerns the relation between polarity and emotions. We apply a novel approach which arranges neutrality, polarity and emotions hierarchically. This method significantly outperforms the corresponding “flat” approach which does not take into account the hierarchical information. We also compare corpus-based and lexical-based feature sets and we choose the most appropriate set of features to be used in our hierarchical classification experiments. Keywords: Sentiment analysis, emotion in text, emotion recognition, text classification, hierarchical classification.
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