Automatic Acquisition of Adjacent Information and Its Effectiveness in Extraction of Bilingual Word Pairs from Parallel Corpora
We propose a learning method for solving the sparse data problem in automatic extraction of bilingual word pairs from parallel corpora. In general, methods based on similarity measures are insufficient because of the sparse data problem. The essence of our method is the use of this inference: in local parts of bilingual sentence pairs (e.g., phrases, not sentences), the equivalents of words that adjoin the source language words of bilingual word pairs also adjoin the target language words of bilingual word pairs. Our learning method automatically acquires such adjacent information. The acquired adjacent information is used to extract bilingual word pairs. As a result, our system can limit the search scope for the decision of equivalents in bilingual sentence pairs by extracting only word pairs that adjoin the acquired adjacent information. We applied our method to two systems based on Yates’ χ 2 and AIC. Results of evaluation experiments indicate that the extraction rates respectively improved 6.1 and 6.0 percentage points using our method.
KeywordsExtraction Rate Word Pair Machine Translation Parallel Corpus Search Scope
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