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<Paper uid="H05-1039">
  <Title>Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 307-314, Vancouver, October 2005. c(c)2005 Association for Computational Linguistics Combining Deep Linguistics Analysis and Surface Pattern Learning: A Hybrid Approach to Chinese Definitional Question Answering</Title>
  <Section position="2" start_page="0" end_page="307" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Due to the ever increasing large amounts of online textual data, learning from textual data is becoming more and more important. Traditional document retrieval systems return a set of relevant documents and leave the users to locate the specific information they are interested in. Question answering, which combines traditional document retrieval and information extraction, solves this problem directly by returning users the specific answers. Research in textual question answering has made substantial advances in the past few years (Voorhees, 2004).</Paragraph>
    <Paragraph position="1"> Most question answering research has been focusing on factoid questions where the goal is to return a list of facts about a concept. Definitional questions, however, remain largely unexplored. Definitional questions differ from factoid questions in that the goal is to return the relevant &amp;quot;answer nuggets&amp;quot; of information about a query. Identifying such answer nuggets requires more advanced language processing techniques. Definitional QA systems are not only interesting as a research challenge. They also have the potential to be a valuable complement to static knowledge sources like encyclopedias. This is because they create definitions dynamically, and thus answer definitional questions about terms which are new or emerging (Blair-Goldensoha et al., 2004).</Paragraph>
    <Paragraph position="2"> One success in factoid question answering is pattern based systems, either manually constructed (Soubbotin and Soubbotin, 2002) or machine learned (Cui et al., 2004). However, it is unknown whether such pure pattern based systems work well on definitional questions where answers are more diverse.</Paragraph>
    <Paragraph position="3"> Deep linguistic analysis has been found useful in factoid question answering (Moldovan et al., 2002) and has been used for definitional questions (Xu et al., 2004; Harabagiu et al., 2003). Linguistic analy- null sis is useful because full parsing captures long distance dependencies between the answers and the query terms, and provides more information for inference. However, merely linguistic analysis may not be enough. First, current state of the art linguistic analysis such as parsing, co-reference, and relation extraction is still far below human performance. Errors made in this stage will propagate and lower system accuracy. Second, answers to some types of definitional questions may have strong local dependencies that can be better captured by surface patterns. Thus we believe that combining linguistic analysis and pattern learning would be complementary and be beneficial to the whole system.</Paragraph>
    <Paragraph position="4"> Work in combining linguistic analysis with patterns include Weischedel et al. (2004) and Jijkoun et al. (2004) where manually constructed patterns are used to augment linguistic features. However, manual pattern construction critically depends on the domain knowledge of the pattern designer and often has low coverage (Jijkoun et al., 2004). Automatic pattern derivation is more appealing (Ravichandran and Hovy, 2002).</Paragraph>
    <Paragraph position="5"> In this work, we explore a hybrid approach to combining deep linguistic analysis with automatic pattern learning. We are interested in answering the following four questions for Chinese definitional question answering: a0 How helpful are linguistic analysis and pattern learning in definitional question answering? a0 If pattern learning is useful, what kind of question can pattern matching answer? a0 How much human annotation is required for a pattern based system to achieve reasonable performance? null a0 If linguistic analysis is helpful, what linguistic features are most useful? To our knowledge, this is the first formal study of these questions in Chinese definitional QA. To answer these questions, we perform extensive experiments on Chinese TDT4 data (Linguistic Data Consortium, 2002-2003). We separate definitional questions into biographical (Who-is) questions and other definitional (What-is) questions. We annotate some question-answer snippets for pattern learning and we perform deep linguistic analysis including parsing, tagging, name entity recognition, co-reference, and relation detection.</Paragraph>
  </Section>
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