Word Translation Disambiguation Using Bilingual Bootstrapping

Word Translation Disambiguation Using Bilingual Bootstrapping Cong Li Microsoft Research Asia 5F Sigma Center, No.49 Zhichun Road, Haidian Beijing, China, 100080 i-congl@microsoft.com Hang Li Microsoft Research Asia 5F Sigma Center, No.49 Zhichun Road, Haidian Beijing, China, 100080 hangli@microsoft.com Abstract This paper proposes a new method for word translation disambiguation using a machine learning technique called ‘Bilingual Bootstrapping’. Bilingual Bootstrapping makes use of � in learning� a small number of classified data and a large number of unclassified data in the source and the target languages in translation. It constructs classifiers in the two languages in parallel and repeatedly boosts the performances of the classifiers by further classifying data in each of the two languages and by exchanging between the two languages information regarding the classified data. Experimental results indicate that word translation disambiguation based on Bilingual Bootstrapping consistently and significantly outperforms the existing methods based on ‘Monolingual Bootstrapping’. 1 Introduction We address here the problem of word translation disambiguation. For instance, we are concerned with an ambiguous word in English (e.g., ‘plant’), which has multiple translations in Chinese (e.g., ‘�� (gongchang)’ and ‘�� (zhiwu)’). Our goal is to determine the correct Chinese translation of the ambiguous English word, given an English sentence which contains the word. Word translation disambiguation is actually a special case of word sense disambiguation (in the example above, ‘gongchang’ corresponds to the sense of ‘factory’ and ‘zhiwu’ corresponds to the sense of ‘vegetation’).1 Yarowsky (1995) proposes a method for word sense (translation) disambiguation that is based on a bootstrapping technique, which we refer to here as ‘Monolingual Bootstrapping (MB)’. In this paper, we propose a new method for word translation disambiguation using a bootstrapping technique we have developed. We refer to the technique as ‘Bilingual Bootstrapping (BB)’. In order to evaluate the performance of BB, we conducted some experiments on word translation disambiguation using the BB technique and the MB technique. All of the results indicate that BB consistently and significantly outperforms MB. 2 Related Work The problem of word translation disambiguation (in general, word sense disambiguation) can be viewed as that of classification and can be addressed by employing a supervised learning method. In such a learning method, for instance, an English sentence containing an ambiguous English word corresponds to an example, and the Chinese translation of the word under the context corresponds to a classification decision (a label). Many methods for word sense disambiguation using a supervised learning technique have been proposed. They include those using Naïve Bayes (Gale et al. 1992a), Decision List (Yarowsky 1994), Nearest Neighbor (Ng and Lee 1996), Transformation Based Learning (Mangu and Brill 1997), Neural Network (Towell and 1 In this paper, we take English-Chinese translation as example; it is a relatively easy process, however, to extend the discussions to translations between other language pairs. Computational Linguistics (ACL), Philadelphia, July 2002, pp. 343-351. Proceedings of the 40th Annual Meeting of the Association for Voorhess 1998), Winnow (Golding and Roth 1999), Boosting (Escudero et al. 2000), and Naïve Bayesian Ensemble …

Content truncated to 500 words