By Atefeh Farzindar, Vlado Keselj
This e-book constitutes the refereed complaints 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 provided 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 type; textual content summarization and IR; reasoning and e-commerce; probabilistic laptop studying; neural networks and swarm optimization; computing device studying and information mining; typical language processing; textual content analytics; reasoning and making plans; e-commerce; semantic internet; 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,
Emotional classification is not exemplified. Aman and Szpakowicz [3, 11] have shown that the best results on a dataset annotated with emotions are achieved by a combination of corpus-based unigram features and lexical-based features using Support Vector Machine (SVM). In general, 42 D. Ghazi, D. Inkpen, and S. Szpakowicz two supervised machine learning algorithms, SVM and Naïve Bayes, have long been a method of choice for sentiment recognition at the text level [3, 5, 12, 13]. So far most of the research has been concentrated on the feature selections and applying lexical semantics rather than focusing on different learning schemes.
Luo Table 1. Three-way decision results with λ = 1 accept further-exam Reject Total Actually legitimate Actually spam 465 28 22 13 12 227 499 268 Total 493 35 239 767 Table 2. Naive Bayesian results with λ = 1 Classiﬁed legitimate Classiﬁed spam Total 5 Actually legitimate Actually spam 476 32 23 236 499 268 Total 508 259 767 Experimental Results and Evaluations Our experiments were performed on a spambase data set from UCI Machine Learning Repository . The data set consists of 4601 instances, with 1813 instances as spam, and 2788 instances as legitimate, each instance is described by 58 attributes.
They mainly learn the dominant class and if they see an instance they do not have information about, they will classify it as an instance of the bigger class, since it is more probable. The hierarchical classification approach was better at dealing with the highly unbalanced data. In the future, we plan to expand our work by testing on other available emotionannotated data sets. There are three other available datasets annotated with emotions. The first one is a data set of 700 sentences extracted from blogs, which are annotated with nine emotions and one neutral class .