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 pupil symposium and the abstracts of three keynote displays have been conscientiously reviewed and chosen from ninety submissions. The papers are equipped in topical sections on textual content type; textual content summarization and IR; reasoning and e-commerce; probabilistic computing device studying; neural networks and swarm optimization; computing device studying and information mining; traditional language processing; textual content analytics; reasoning and making plans; e-commerce; semantic net; laptop studying; and knowledge mining.

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That is, for every x, τ (x) takes one of the actions, a1 , . . , am . 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.

Zhou, Y. Yao, and J. Luo To obtain a compact form of the decision rules, we denote the three expressions in these conditions by the following three parameters: (λPN − λBN ) , (λPN − λBN ) + (λBP − λPP ) (λBN − λNN ) β= , (λBN − λNN ) + (λNP − λBP ) (λPN − λNN ) . γ= (λPN − λNN ) + (λNP − λPP ) α= (15) The decision rules (P)-(N) can be expressed concisely as: (P) If P r(C|x) ≥ α and P r(C|x) ≥ γ, decide x ∈ POS(C); (B) If P r(C|x) ≤ α and P r(C|x) ≥ β, decide x ∈ BND(C); (N) If P r(C|x) ≤ β and P r(C|x) ≤ γ, decide x ∈ NEG(C).

We have combined all the results in Figure 1, which displays the F-measure of each class for the three approaches. 5 Conclusions and Future Work The focus of this study was an emotional analysis and classification of emotions in sentences. We first defined two different sets of lexicon-based features to compare with the bag-of-words classification method. As a result, we noticed that unigram features were not much better than polarity lexicon features. Besides, the polarity lexicon features had some benefits over unigrams.

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