Collocation Translation Acquisition Using Monolingual Corpora.pdfVIP

Collocation Translation Acquisition Using Monolingual Corpora Yajuan Lü Microsoft Research Asia 5F Sigma Center, No. 49 Zhichun Road, Haidian District, Beijing, China, 100080 t-yjlv@ Ming ZHOU Microsoft Research Asia 5F Sigma Center, No. 49 Zhichun Road, Haidian District, Beijing, China, 100080 mingzhou@ Abstract Collocation translation is important for machine translation and many other NLP tasks. Unlike previous methods using bilingual parallel corpora, this paper presents a new method for acquiring collocation translations by making use of monolingual corpora and linguistic knowledge. First, dependency triples are extracted from Chinese and English corpora with dependency parsers. Then, a dependency triple translation model is estimated using the EM algorithm based on a dependency correspondence assumption. The generated triple translation model is used to extract collocation translations from two monolingual corpora. Experiments show that our approach outperforms the existing monolingual corpus based methods in dependency triple translation and achieves promising results in collocation translation extraction. 1 Introduction A collocation is an arbitrary and recurrent word combination (Benson, 1990). Previous work in collocation acquisition varies in the kinds of collocations they detect. These range from two- word to multi-word, with or without syntactic structure (Smadja 1993; Lin, 1998; Pearce, 2001; Seretan et al. 2003). In this paper, a collocation refers to a recurrent word pair linked with a certain syntactic relation. For instance, solve, verb-object, problem is a collocation with a syntactic relation verb-object. Translation of collocations is difficult for non- native speakers. Many collocation translations are idiosyncratic in the sense that they are unpredictable by syntactic or semantic features. Consider Chinese to English translation. The translations of “解决” can be “solve” or “resolve”. The tra