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<Paper uid="J04-1001">
  <Title>c(c) 2004 Association for Computational Linguistics Word Translation Disambiguation Using Bilingual Bootstrapping</Title>
  <Section position="2" start_page="0" end_page="2" type="abstr">
    <SectionTitle>
1. Introduction
</SectionTitle>
    <Paragraph position="0"> We address here the problem of word translation disambiguation. If, for example, we were to attempt to translate the English noun plant, which could refer either to a type of factory or to a form of flora (i.e., in Chinese, either to [gongchang]orto [zhiwu]), our goal would be to determine the correct Chinese translation. That is, word translation disambiguation is essentially a special case of word sense disambiguation (in the above example, gongchang would correspond to the sense of factory and zhiwu to the sense of flora).</Paragraph>
    <Paragraph position="1">  We could view word translation disambiguation as a problem of classification. To perform the task, we could employ a supervised learning method, but since to do so would require human labeling of data, which would be expensive, bootstrapping would be a better choice.</Paragraph>
    <Paragraph position="2"> Yarowsky (1995) has proposed a bootstrapping method for word sense disambiguation. When applied to translation from English to Chinese, his method starts learning with a small number of English sentences that contain ambiguous English words and that are labeled with correct Chinese translations of those words. It then uses these classified sentences as training data to create a classifier (e.g., a decision list), which it uses to classify unclassified sentences containing the same ambiguous words. The output of this process is then used as additional training data. It also adopts the one-sense-per-discourse heuristic (Gale, Church, and Yarowsky 1992b) in classifying unclassified sentences. By repeating the above process, an accurate classifier for word translation disambiguation can be created. Because this method uses data in a single language (i.e., the source language in translation), we refer to it here as monolingual bootstrapping (MB).</Paragraph>
    <Paragraph position="3"> [?] 5F Sigma Center, No. 49 Zhichun Road, Haidian, Beijing, China, 100080. E-mail:{hangli,i-congl}@ microsoft.com.</Paragraph>
    <Paragraph position="4"> 1 In this article, we take English-Chinese translation as an example; but the ideas and methods described here can be applied to any pair of languages.</Paragraph>
    <Paragraph position="5">  Computational Linguistics Volume 30, Number 1 In this paper, we propose a new method of bootstrapping, one that we refer to as bilingual bootstrapping (BB). Instead of using data in one language, BB uses data in two languages. In translation from English to Chinese, for example, BB makes use of unclassified data from both languages. It also uses a small number of classified data in English and, optionally, a small number of classified data in Chinese. The data in the two languages should be from the same domain but are not required to be exactly in parallel.</Paragraph>
    <Paragraph position="6"> BB constructs classifiers for English-to-Chinese translation disambiguation by repeating the following two steps: (1) Construct a classifier for each of the languages on the basis of classified data in both languages, and (2) use the constructed classifier for each language to classify unclassified data, which are then added to the classified data of the language. We can use classified data in both languages in step (1), because words in one language have translations in the other, and we can transform data from one language into the other.</Paragraph>
    <Paragraph position="7"> We have experimentally evaluated the performance of BB in word translation disambiguation, and all of our results indicate that BB consistently and significantly outperforms MB. The higher performance of BB can be attributed to its effective use of the asymmetric relationship between the ambiguous words in the two languages. Our study is organized as follows. In Section 2, we describe related work. Specifically, we formalize the problem of word translation disambiguation as that of classification based on statistical learning. As examples, we describe two such methods: one using decision lists and the other using naive Bayes. We also explain the Yarowsky disambiguation method, which is based on Monolingual Bootstrapping. In Section 3, we describe bilingual bootstrapping, comparing BB with MB, and discussing the relationship between BB and co-training. In Section 4, we describe our experimental results, and finally, in Section 5, we give some concluding remarks.</Paragraph>
  </Section>
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