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

By Atefeh Farzindar, Vlado Keselj

This ebook 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 awarded 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 prepared in topical sections on textual content category; textual content summarization and IR; reasoning and e-commerce; probabilistic laptop studying; neural networks and swarm optimization; laptop studying and knowledge mining; common language processing; textual content analytics; reasoning and making plans; e-commerce; semantic internet; desktop studying; and knowledge mining.

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1]. We split the spambase data set into a training set of 3834 instances, and a testing set of 767 instances. Since the attributes in the input data set have continuous values, entropy-MDL [4] is used as the discretization method applied to both the training and testing data sets before the calculations of probabilities. For the cost-sensitive evaluations, we assume that misclassifying a legitimate email as spam is λ times more costly than misclassifying a spam email as legitimate. We considered three different λ values (λ = 9, λ = 3, and λ = 1) for the original naive Bayesian spam filter.

These legal experts often summarize decisions and look for information relevant to specific cases in these summaries. The high quality required for these summaries cannot be achieved by commonly available automatic summarization methods as was shown by Farzindar [2] who compared different summarization methods whose results were evaluated by legal experts. Using these results, NLP Technologies Inc. has developed a summarization system, named DecisionExpressTM , based on a thematic segmentation of the text, specifically tailored to the legal field.

Following the discussions given in the book by Duda and Hart [3], the basic ideas of the theory are reviewed. Let Ω = {w1 , . . , ws } be a finite set of s states and let A = {a1 , . . , am } be a finite set of m possible actions. Let λ(ai |wj ) denote the loss, or cost, for taking action ai when the state is wj . Let P r(wj |x) be the conditional probability of an email being in state wj given that the email is described by x. For an email with description x, suppose action ai is taken. Since P r(wj |x) is the probability that the true state is wj given x, the expected loss associated with taking action ai is given by: s R(ai |x) = λ(ai |wj )P r(wj |x).

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