By Aijun An, Nick Cercone, Xiangji Huang (auth.), Eleni Stroulia, Stan Matwin (eds.)
AI 2001 is the 14th within the sequence of Arti cial Intelligence meetings subsidized by way of the Canadian Society for Computational experiences of Intelligence/Soci et e - nadienne pour l’ etude de l’intelligence par ordinateur. As used to be the case final 12 months too, the convention is being held at the side of the yearly meetings of 2 different Canadian societies, snap shots Interface (GI 2001) and imaginative and prescient Int- face (VI 2001). We think that the general event may be enriched by means of this conjunction of meetings. This yr is the \silver anniversary" of the convention: the rst Canadian AI convention used to be held in 1976 at UBC. in the course of its lifetime, it has attracted Canadian and overseas papers of top quality from various AI learn parts. All papers submitted to the convention bought a minimum of 3 indep- dent studies. nearly one 3rd have been accredited for plenary presentation on the convention. the simplest paper of the convention could be invited to seem in Computational Intelligence.
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Extra resources for Advances in Artificial Intelligence: 14th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2001 Ottawa, Canada, June 7–9, 2001 Proceedings
Malich. Simulated Trading: A New Approach For Solving Vehicle Routing Problems. 125, Mathematisches Institut der Universität zu Köln, Dezember 1992. -J. Bürckert, K. Fischer, and G. Vierke. Transportation Scheduling with Holonic MAS — The TeleTruck Approach. In: Proceedings of the Third International Conference on Practical Applications of Intelligent Agents and Multiagents (PAAM´98), 1998. K. Fischer, C. Russ, and G. Vierke. Decision Theory and Coordination in Multi-agent Systems. Research Report RR-98-02, DFKI, 1998.
A Case in the Maintenance of Discovered Association Rules, Data Mining and Knowledge Discovery, 1998, pp. 233-262. , An Effective Hashed Based Algorithm for Mining Association Rules, In Proceedings of the ACM SIGMOD Conference on Management of Data, San Jose, California, may, 1995. ca Abstract. Term extraction is an important problem in natural language processing. In this paper, we propose a language independent statistical corpusbased term extraction algorithm. In previous approaches, evaluation has been subjective, at best relying on a lexicographer’s judgement.
E. with an empty term list). On both corpora, the term list generated by our system significantly reduces perplexity. We also experimented with a different expansion function for our term extraction algorithm. Instead of using the log-likelihood ratio as in Step 1 of the algorithm presented in Figure 3, we used the mutual information metric. The third column of Table 1 shows the result. This metric performs much worse on the smaller NAG corpus. This supports the claim from Section 3 that mutual information deteriorates with sparse data.