By Dekang Lin (auth.), Atefeh Farzindar, Vlado Kešelj (eds.)
This publication constitutes the refereed court cases of the twenty third convention on synthetic 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 rigorously reviewed and chosen from ninety submissions. The papers are geared up in topical sections on textual content type; textual content summarization and IR; reasoning and e-commerce; probabilistic computer studying; neural networks and swarm optimization; computing device studying and knowledge mining; usual language processing; textual content analytics; reasoning and making plans; e-commerce; semantic net; computing device studying; and information mining.
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Additional info for Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings
11. : Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (2008) ISBN13: 9781558605527 12. : Forbidden American English (1991) ISBN: 9780844251493 13. : Recognizing subjectivity: a case study in manual tagging. Natural Language Engineering 5(2) (1999) 14. : Development and use of a gold standard data set for subjectivity classifications. In: Proc. 37th Annual Meeting of the Assoc. for Computational Linguistics (ACL 1999), pp. 246–253 (1999) 15. : Thumbs up? Sentiment classification using machine learning techniques.
G. g. g. g. g. “queer”) and other matters; [26, 27] try to examine only the pattern of uses of “fuck” and its morphological variants, because Offensive Language Detection Using Multi-level Classification 19 this is a typical swear-word that occurs frequently in the British National Corpus (BNC). Also McEnery et. Al. in this article try to build and expand upon the examination of “fuck” [28, 29] by examining the distribution pattern of “fuck” within and across spoken and written registers. Specifically as flame detection systems, we should name Smokey  which probably is still being used by Microsoft in commercial applications.
Experiments on document categorization and question classification exhibit the effectiveness of the enhanced ECOC framework. In the future, we plan to conduct more experiments on more datasets. Our approach is expected to be useful for non-text applications, but it needs to be verified with extensive experiments. We also plan to explore other local search algorithms to further improve the proposed strategy. References 1. : Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002) 2.
Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings by Dekang Lin (auth.), Atefeh Farzindar, Vlado Kešelj (eds.)