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 means 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 along with the yearly meetings of 2 different Canadian societies, portraits Interface (GI 2001) and imaginative and prescient Int- face (VI 2001). We think that the general event could be enriched through 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 overseas papers of top of the range from various AI study components. All papers submitted to the convention obtained at the very least 3 indep- dent stories. nearly one 3rd have been approved for plenary presentation on the convention. the simplest paper of the convention can 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
However, if p is not extended then p is classified as a term and added to E. So far, the algorithm is purely statistical. However, the final part of Step 2 provides the option of using linguistic knowledge to filter the extracted terms. For example, if the statistical processor treats punctuation marks as words, it is probable that some extended features will contain punctuation marks. This filter allows for easy removal of such erroneous terms. The final step of the extension algorithm simply determines and returns whether or not c was extracted in whole or in part as a term.
We used Witten-Bell discounting  to estimate the probability of unseen events. 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.
Using our term extractor, we extract a list of terms from a training corpus. To compute m, we require the frequency counts of all words and terms. We obtain these frequencies by counting all terms and words that are not terms in the training corpus. Using a testing corpus L with a finite number of words n, we approximate the crossentropy H(p, m) from formula (5) with: 42 P. Pantel and D. Lin Term Effe ct on Pe rplexity 665 660 655 650 645 1 41 81 121 161 201 241 281 Ranke d Te rms by our Extractor Fig.
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 by Aijun An, Nick Cercone, Xiangji Huang (auth.), Eleni Stroulia, Stan Matwin (eds.)