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<Paper uid="N06-1011">
  <Title>Named Entity Transliteration and Discovery from Multilingual Comparable Corpora</Title>
  <Section position="3" start_page="83" end_page="83" type="intro">
    <SectionTitle>
2 Previous Work
</SectionTitle>
    <Paragraph position="0"> There has been other work to automatically discover NE with minimal supervision. Both (Cucerzan and Yarowsky, 1999) and (Collins and Singer, 1999) present algorithms to obtain NEs from untagged corpora. However, they focus on the classification stage of already segmented entities, and make use of contextual and morphological clues that require knowledge of the language beyond the level we want to assume with respect to the target language.</Paragraph>
    <Paragraph position="1"> The use of similarity of time distributions for information extraction, in general, and NE extraction, in particular, is not new. (Hetland, 2004) surveys recent methods for scoring time sequences for similarity. (Shinyama and Sekine, 2004) used the idea to discover NEs, but in a single language, English, across two news sources.</Paragraph>
    <Paragraph position="2"> A large amount of previous work exists on transliteration models. Most are generative and consider the task of producing an appropriate transliteration for a given word, and thus require considerable knowledge of the languages. For example, (AbdulJaleel and Larkey, 2003; Jung et al., 2000) train English-Arabic and English-Korean generative transliteration models, respectively. (Knight and Graehl, 1997) build a generative model for backward transliteration from Japanese to English.</Paragraph>
    <Paragraph position="3"> While generative models are often robust, they tend to make independence assumptions that do not hold in data. The discriminative learning framework argued for in (Roth, 1998; Roth, 1999) as an alternative to generative models is now used widely in NLP, even in the context of word alignment (Taskar et al., 2005; Moore, 2005). We make use of it here too, to learn a discriminative transliteration model that requires little knowledge of the target language.</Paragraph>
  </Section>
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