Modifying Existing Analogy-based N-gram Language Model

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概要

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• By investigating the occurrence of different proportional analogies in corpora, this paper describes an approach to increase the performance of existing analogy-based N-gram language models evaluated by perplexity. Our approach consists in using analogy to reconstruct N-grams from the test data so as to give higher probabilities to these N-grams. By giving different weights to different patterns, we also except that some N-grams which can be reconstructed by different patterns will get more accurate probabilities. The use of suffix arrays for data searching leads to a lesser computation time on text scoring tasks.

• 2014-01-30

著者

• Meng Tian

  IPS, Waseda University

• Yves Lepage

  IPS, Waseda University

関連論文

• Modifying Existing Analogy-based N-gram Language Model
• Hierarchical Statistical Machine Translation Using Sampling Based Alignment

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