Advances in Artificial Intelligence: 14th Biennial by Aijun An, Nick Cercone, Xiangji Huang (auth.), Eleni

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 through the Canadian Society for Computational stories of Intelligence/Soci et e - nadienne pour l’ etude de l’intelligence par ordinateur. As was once the case final 12 months too, the convention is being held along side the once a year meetings of 2 different Canadian societies, pictures Interface (GI 2001) and imaginative and prescient Int- face (VI 2001). We think that the general adventure may be enriched by means of this conjunction of meetings. This 12 months 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 foreign papers of top of the range from various AI examine parts. All papers submitted to the convention bought not less than 3 indep- dent reports. nearly one 3rd have been authorized for plenary presentation on the convention. the easiest paper of the convention could be invited to seem in Computational Intelligence.

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Additional info 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

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Step 3: Compute the rest of the itemsets in the candidate itemsets that may be large itemsets in the new database. Since by the end of step 2 above, we reduced the sizes of all level small itemsets and candidate sets, the algorithm now takes each small itemset Si = Ci − Li and scans the new database to determine if these itemsets are large in the new database. If they are large, they are included in the appropriate level new large itemset Li . – Step 4: Adjusting all level i candidate sets to include the new large itemsets previously small in the old database at level (i-1).

1 29 Apriori and FUP2 on Sample Database Suppose we have a database DB with set of items, I=A, B, C, D, E, F and MinSupport=3 transactions. A simple database transaction table for illustrating the idea is given as Table 1. Table 1. The Example Database Transaction table TID 100 200 300 400 500 600 A A B A A A Items BCDF BCDE CD DE CD BCE If we delete transacion 400, and add new transaction 700 B D E, we can get an updated database DB’, which is suitable for FUP2. To compute the large itemsets in the old database (Table 1), Apriori algorithm first generates the candidate set C1 ={A, B, C, D, E, F}, then scans the database to obtain the support of each itemset in C1 .

AI 2001, LNAI 2056, pp. 36-46, 2001. © Springer-Verlag Berlin Heidelberg 2001 A Statistical Corpus-Based Term Extractor 2 37 Previous Work There have been several approaches to automatic term extraction mostly for technical terminology and noun phrases. Many successful algorithms are statistical corpusbased approaches [2], [5], [10]. Several term extraction tools have been developed. Dagan and Church [4] proposed a tool that assists terminologists in identifying and translating technical terms.

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