Download Advances in Artificial Intelligence: 23rd Canadian by Atefeh Farzindar, Vlado Keselj PDF

By Atefeh Farzindar, Vlado Keselj

This e-book constitutes the refereed complaints 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 pupil symposium and the abstracts of three keynote shows have been conscientiously reviewed and chosen from ninety submissions. The papers are prepared in topical sections on textual content class; textual content summarization and IR; reasoning and e-commerce; probabilistic laptop 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 internet; computing device studying; and knowledge 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,

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So we cannot always rely on the order of the source sentences for matching. Sentence similarities. In the legal field, it is common to see repetition (phrases, clauses or even whole sentences) within a document. Therefore, when identifying sentences, selecting the first text and extract pair that matches may not be the best heuristic. We also have to exploit other clues like ordering. To deal with those issues, we went through several attempts to identify target sentences while we gradually discovered problematic cases.

BN − λNN λPN − λBN (17) The condition (c1) implies that 1 ≥ α > γ > β ≥ 0. In this case, after tiebreaking, the following simplified rules are obtained [17]: (P1) (B1) If P r(C|x) ≥ α, decide x ∈ POS(C); If β < P r(C|x) < α, decide x ∈ BND(C); (N1) If P r(C|x) ≤ β, decide x ∈ NEG(C). The parameter γ is no longer needed. From the rules (P1), (B1), and (N1), the (α, β)-probabilistic positive, negative and boundary regions are given, respectively, by: POS(α,β) (C) = {x ∈ U | P r(C|x) ≥ α}, BND(α,β) (C) = {x ∈ U | β < P r(C|x) < α}, NEG(α,β) (C) = {x ∈ U | P r(C|x) ≤ β}.

We are not interested in having better results by adding more features. What is more interesting is to find a list of fewer but more meaningful features which could contribute more to learning. In our experiments, the size of polarity features is quite smaller than the unigrams. Also, by checking the features manually we noticed that they appear to be more meaningful. For example, among the unigram features we have proper nouns such as names of people and countries. It is also possible to have misspelled tokens in unigrams, while the prior-polarity lexicon features are welldefined words usually considered as polar.

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