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<Paper uid="P01-1070">
  <Title>Using Machine Learning Techniques to Interpret WH-questions</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
2 Related Research
</SectionTitle>
    <Paragraph position="0"> Our research builds on earlier work on the use of probabilistic models to understand free-text queries in search applications (Heckerman and Horvitz, 1998; Horvitz et al., 1998), and on work conducted in the IR arena of question answering (QA) technologies.</Paragraph>
    <Paragraph position="1"> Heckerman and Horvitz (1998) and Horvitz et al. (1998) used hand-crafted models and supervised learning to construct Bayesian models that predict users' goals and needs for assistance in the context of consumer software applications. Heckerman and Horvitz' models considered words, phrases and linguistic structures (e.g., capitalization and definite/indefinite articles) appearing in queries to a help system. Horvitz et al.'s models considered a user's recent actions in his/her use of software, together with probabilistic information maintained in a dynamically updated user profile.</Paragraph>
    <Paragraph position="2"> QA research centers on the challenge of enhancing the response of search engines to a user's questions by returning precise answers rather than returning documents, which is the more common IR goal. QA systems typically combine traditional IR statistical methods (Salton and McGill, 1983) with &amp;quot;shallow&amp;quot; NLP techniques. One approach to the QA task consists of applying the IR methods to retrieve documents relevant to a user's question, and then using the shallow NLP to extract features from both the user's question and the most promising retrieved documents. These features are then used to identify an answer within each document which best matches the user's question. This approach was adopted in (Kupiec, 1993; Abney et al., 2000; Cardie et al., 2000; Moldovan et al., 2000).</Paragraph>
    <Paragraph position="3"> The NLP components of these systems employed hand-crafted rules to infer the type of answer expected. These rules were built by considering the first word of a question as well as larger patterns of words identified in the question.</Paragraph>
    <Paragraph position="4"> For example, the question &amp;quot;How far is Mars?&amp;quot; might be characterized as requiring a reply of type DISTANCE. Our work differs from traditional QA research in its use of statistical models to predict variables that represent a user's informational goals. The variables under consideration include the type of the information requested in a query, the level of detail of the answer, and the parts-of-speech which contain the topic the query and its focus (which resembles the type of the expected answer). In this paper, we focus on the predictive models, rather than on the provision of answers to users' questions. We hope that in the short term, the insights obtained from our work will assist QA researchers to fine-tune the answers generated by their systems.</Paragraph>
  </Section>
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