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This quantity relies at the Workshop on Evolutionary Computing held in Sheffield, U. okay. , in April 1995 lower than the sponsorship of the Society for the learn of man-made Intelligence and Simulation of habit (AISB). The 18 complete papers provided have been chosen in the course of a post-workshop refereeing assembly and selected from 32 submissions for the workshop.
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Springer (2003) 223–241. 20. , Evans, D. A: Automatic Transliteration for Japanese-to-English Text Retrieval. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2003) 353–360. 21. , Hull, D. , Evans, D. : Towards Effective Strategies for Monolingual and Bilingual Information Retrieval: Lessons Learned from NTCIR-4. ACM Transactions on Asian Language Information Processing. (to appear) 22. , Vines, P: Using the web for automated translation extraction in cross-language information retrieval.
19. , Evans, D. : Resolving Translation Ambiguity Using Monolingual Corpora. , Gonzalo, J. (eds): Advances in Cross-Language Information Retrieval: Third Workshop of the Cross-Language Evaluation Forum, CLEF 2002, Rome, Italy, September 19–20, 2002. Lecture Notes in Computer Science, Vol 2785. Springer (2003) 223–241. 20. , Evans, D. A: Automatic Transliteration for Japanese-to-English Text Retrieval. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2003) 353–360.
Headline generation can be obtained as a weighted combination of the content and structure model log probabilities shown in formula (3). The system was trained on the 3 months Mainichi Shimbun articles((27,133 articles from Jan. to Mar. 1999) for Japanese corpus. We estimate α, β and γ in formula (3) using 5 cross-validation1. Fig. 1 illustrates sample output using Mainichi Shimbun corpus. Numbers to the right are log probabilities of the word sequence. 2 Tracking by Hierarchical Classification In the TDT tracking task, the number of labeled positive training stories is small (at most 16 stories) compared to the negative training stories.