Academic Paper |
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| Title: | Linguistic Knowledge in Statistical Phrase-based Word Alignment |
| Linguistic Field: | Computational Linguistics; Text/Corpus Linguistics |
| Abstract: | In this paper, a novel phrase alignment strategy combining linguistic knowledge and co-occurrence measures extracted from bilingual corpora is presented. The algorithm is mainly divided into four steps, namely phrase selection and classification, phrase alignment, one-to-one word alignment and post-processing. The first stage selects a linguistically-derived set of phrases that convey a unified meaning during translation and are therefore aligned together in parallel texts. These phrases include verb phrases, idiomatic expressions and date expressions. During the second stage, very high precision links between these selected phrases for both languages are produced. The third step performs a statistical word alignment using association measures and link probabilities with the remaining unaligned tokens, and finally the fourth stage takes final decisions on unaligned tokens based on linguistic knowledge. Experiments are reported for an English-Spanish parallel corpus, with a detailed description of the evaluation measure and manual reference used. Results show that phrase co-occurrence measures convey a complementary information to word co-occurrences and a stronger evidence of a correct alignment, successfully introducing linguistic knowledge in a statistical word alignment scheme. Precision, Recall and Alignment Error Rate (AER) results are presented, outperforming state-of-the-art alignment algorithms. |
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This article appears in Natural Language Engineering Vol. 12, Issue 1, which you can read on Cambridge's site or on LINGUIST . |
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