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<Paper uid="P05-2005">
  <Title>Exploiting Named Entity Taggers in a Second Language</Title>
  <Section position="2" start_page="0" end_page="25" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Given the usefulness of Named Entities (NEs) in many natural language processing tasks, there has been a lot of work aimed at developing accurate named entity extractors (Borthwick, 1999; Velardi et al., 2001; Ar'evalo et al., 2002; Zhou and Su, 2002; Florian, 2002; Zhang and Johnson, 2003). Most approaches however, have very low portability, they are designed to perform well over a particular collection or type of document, and their accuracies will drop considerably when used in different domains.</Paragraph>
    <Paragraph position="1"> The reason for this is that many NE extractor systems rely heavily on complex linguistic resources, which are typically hand coded, for example regular expressions, grammars, gazetteers and the like.</Paragraph>
    <Paragraph position="2"> Adapting a system of this nature to a different collection or language requires a lot of human effort, involving tasks such as rewriting the grammars, acquiring new dictionaries, searching trigger words, and so on. Even if one has the human resources and the time needed for the adaptation process, there are languages that lack the linguistic resources needed, for instance, dictionaries are available in electronic form for only a handful of languages. We believe that, by using machine learning techniques, we can adapt an existing hand coded system to different domains and languages with little human effort.</Paragraph>
    <Paragraph position="3"> Our goal is to present a method that will facilitate the task of increasing the coverage of named entity extractor systems. In this setting, we assume that we have available an NE extractor system for Spanish, and we want to adapt it so that it can perform NER accurately in documents from a different language, namely Portuguese. It is important to emphasize here that we try to avoid the use of complex and costly linguistic tools or techniques, besides the existing NER system, given the language restrictions they pose. Although, we do need a corpus of the target language. However, we consider the task of gathering a corpus much easier and faster than that of developing linguistic tools such as parsers, part-of-speech taggers, grammars and the like.</Paragraph>
    <Paragraph position="4"> In the next section we present some recent work related to NER. Section 3 describes the data sets used in our experiments. Section 4 introduces our approach to NER, and we conclude in Section 5 giving a brief discussion of our findings and proposing research lines for future work.</Paragraph>
  </Section>
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