Cryptanalysis Of Speed C Hall J Kelsey V Rijmen B Schneier

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In Proceedings of the Workshop on Very Large Corpora: Academic and Industrial Perspectives (1993) 1–8. 13. Jeong, K. , Myaeng, S, Lee, J. , Choi, K. : Automatic Identification and Backtransliteration of Foreign Words for Information Retrieval. Information Processing and Management, 35(4) (1999) 523–540. 14. : Machine Transliteration. Computational Linguistics: 24(4) (1998) 599–612. 15. : Building an MT dictionary from Parallel Texts Based on Linguistic and Statistical Information. In Proceedings of the 15th International Conference on Computational Linguistics (COLING) (1994) 76–81.

Their method combines the output of a diverse set of classifiers and tuning parameters for the combined system on a retrospective corpus. The idea comes from the well-known practice in information retrieval and speech recognition of combining the output of a large number of systems to yield a better result than the individual system’s output. They reported that the new variants of kNN reduced up to 71% in weighted error rates on the TDT3-dryrun corpus. 12 F. Fukumoto and Y. e. using content compression rather than on corpus statistics to detect relevance and assess topicality of the source material [16].

Stories’ shows the result using all words in the stories and ‘Original headlines’ shows the result using the original headlines in the stories. e. the output of headline generation, six words, and named entities, Person and Proper name. Table 8 shows that our method outperformed the other two methods, especially attained a better balance between recall and precision. Table 9 illustrates changes in pooled F1 measure as Nt varies, with Nt = 4 as the baseline. Table 9 shows that our method is the most stable all Nt training instances before Nt = 16, especially our method is effective even for a small number of positive training instances for per-source training: it learns a good topic representation and gains almost nothing in effectiveness beyond Nt = 16.

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