Mining Key Phrase Translations from Web Corpora

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 483–490, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics Mining Key Phrase Translations from Web Corpora Fei Huang Ying Zhang Stephan Vogel School of Computer Science Carnegie Mellon University, Pittsburgh, PA 15213 {fhuang, joy, vogel}@cs.cmu.edu Abstract Key phrases are usually among the most information-bearing linguistic structures. Translating them correctly will improve many natural language processing appli- cations. We propose a new framework to mine key phrase translations from web corpora. We submit a source phrase to a search engine as a query, then expand queries by adding the translations of topic-relevant hint words from the re- turned snippets. We retrieve mixed- language web pages based on the ex- panded queries. Finally, we extract the key phrase translation from the second- round returned web page snippets with phonetic, semantic and frequency- distance features. We achieve 46% phrase translation accuracy when using top 10 re- turned snippets, and 80% accuracy with 165 snippets. Both results are signifi- cantly better than several existing meth- ods. 1 Introduction Key phrases such as named entities (person, loca- tion and organization names), book and movie ti- tles, science, medical or military terms and others 1, are usually among the most information-bearing linguistic structures. Translating them correctly will improve the performance of cross-lingual in- formation retrieval, question answering and ma- chine translation systems. However, these key phrases are often domain-specific, and people con- 1 Some name and terminology is a single word, which could be regarded as a one-word phrase. stantly create new key phrases which are not cov- ered by existing bilingual dictionaries or parallel corpora, therefore standard data-driven or knowl- edge-based machine translation systems cannot translate them correctly. As an increasing amount of web information be- comes available, exploiting such a huge informa- tion resource is becoming more attractive. (Resnik 1999) searched the web for parallel corpora while (Lu et al. 2002) extracted translation pairs from anchor texts pointing to the same webpage. How- ever, parallel webpages or anchor texts are quite limited, and these approaches greatly suffer from the lack of data. However, there are many web pages containing useful bilingual information where key phrases and their translations both occur. See the example in Figure 1. This example demonstrates web page snippets2 containing both a Chinese key phrase “浮 士德” and its translation, “Faust”. We thus can transform the translation problem into a data mining problem by retrieving these mixed-language web pages and extracting their translations. We propose a new framework to mine key phrase translations from web corpora. Given a source key phrase (here a Chinese phrase), we first retrieve web page snippets containing this phrase using the Google search engine. We then expand queries by adding the translations of topic-relevant hint words from the returned snippets. We submit the source key phrase and expanded queries again to Google to retrieve mixed-language web page snippets. Finally, we extract the key phrase trans- lation from the second-round …

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