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This ebook 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 prepared 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; ordinary language processing; textual content analytics; reasoning and making plans; e-commerce; semantic net; laptop studying; and knowledge mining.
Read Online 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,
Our case is simpler and diﬀerent because sentences are expected to be at least similar so we decided to develop our own method. Source documents being in XML, each sentence is delimited by an
tag. Plain text extract are split into four sections related to the legal themes described above. This diﬀerence gave rise to the following issues: Sentence boundaries detection. g. g. bulleted lists). g. “”). Sentence alterations. When generating the summaries, legal experts may have modiﬁed sentences, even though, in principle, it was forbidden.
They also used statistics gathered from Web search engines  based on the hypothesis that groups of words which co-occur with a given emotion word are highly likely to express the same emotion. Mihalcea and Strapparava  exploited the co-occurrence of words in the text with the words which have explicit affective meaning. They used the WordNet affect list as the direct affective words, and implemented a variation of Latent Semantic Analysis to yield a vector space model which allows for a homogeneous representation of words .
The experiments and results of applying them to our chosen data set will be presented. 1 Two-Level Classification The main goal of this task is to find out how the presence of neutral instances affects the performance of features for distinguishing between emotional classes. This was motivated by a similar work in polarity classification . In the two-level classifications, the first level, emotional versus non-emotional classification, tries to determine whether an instance is neutral or emotional.