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<Paper uid="P06-1129">
  <Title>Exploring Distributional Similarity Based Models for Query Spelling Correction</Title>
  <Section position="3" start_page="0" end_page="1025" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Investigations into query log data reveal that more than 10% of queries sent to search engines contain misspelled terms (Cucerzan and Brill, 2004). Such statistics indicate that a good query speller is crucial to search engine in improving web search relevance, because there is little opportunity that a search engine can retrieve many relevant contents with misspelled terms.</Paragraph>
    <Paragraph position="1"> The problem of designing a spelling correction program for web search queries, however, poses special technical challenges and cannot be well solved by general purpose spelling correction methods. Cucerzan and Brill (2004) discussed in detail specialties and difficulties of a query spell checker, and illustrated why the existing methods could not work for query spelling correction.</Paragraph>
    <Paragraph position="2"> They also identified that no single evidence, either a conventional spelling lexicon or term frequency in the query logs, can serve as criteria for validate queries.</Paragraph>
    <Paragraph position="3"> To address these challenges, we concentrate on the problem of learning improved query spelling correction model by integrating distributional similarity information automatically derived from query logs. The key contribution of our work is identifying that we can successfully use the evidence of distributional similarity to achieve better spelling correction accuracy. We present two methods that are able to take advantage of distributional similarity information. The first method extends a string edit-based error model with confusion probabilities within a generative source channel model. The second method explores the effectiveness of our approach within a discriminative maximum entropy model framework by integrating distributional similarity-based features. Experimental results demonstrate that both methods can significantly outperform their baseline systems in the spelling correction task for web search queries.</Paragraph>
    <Paragraph position="4">  The rest of the paper is structured as follows: after a brief overview of the related work in Section 2, we discuss the motivations for our approach, and describe two methods that can make use of distributional similarity information in Section 3. Experiments and results are presented in Section 4. The last section contains summaries and outlines promising future work.</Paragraph>
  </Section>
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