Automatically acquiring synonymous collocation pairs such as <turn on, OBJ, light> and <switch on, OBJ, light> from corpora is a challenging task. For this task, we can, in general, have a large monolingual corpus and/or a very limited bilingual corpus. Methods that use monolingual corpora alone or use bilingual corpora alone are apparently inadequate because of low precision or low coverage. In this paper, we propose a method that uses both these resources to get an optimal compromise of precision and coverage. This method first gets candidates of synonymous collocation pairs based on a monolingual corpus and a word thesaurus, and then selects the appropriate pairs from the candidates using their translations in a second language. The translations of the candidates are obtained with a statistical translation model which is trained with a small bilingual corpus and a large monolingual corpus. The translation information is proved as effective to select synonymous collocation pairs. Experimental results indicate that the average precision and recall of our approach are 74% and 64% respectively, which outperform those methods that only use monolingual corpora and those that only use bilingual corpora.
Wu and Zhou REF suggested a method that firstly gets candidates of synonymous collocation pairs based on a monolingual corpus and then selects the appropriate pairs of the candidates using their translations in a second language.
Synonymous Collocation Extraction Using Translation Information
Annual Meeting Of The Association For Computational Linguistics
Computer Science