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<Paper uid="W01-1206">
  <Title>Answer Mining from On-Line Documents</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
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2 Background
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    <Paragraph position="0"> Open-Domain Question/Answering To search in a large collection of on-line documents for the answer to a natural language question we need to know (1) what we are looking for, i.e. the expected answer type; and (2) where the answer might be located in the collection. Furthermore, knowing the answer type and recognizing a text passage where the answer might be found is not sufficient for extracting the exact answer. We also need to know the dependencies between the answer type and the other concepts from the question or the answer. For example, if the answer type of the TREC question QT: How many dogs pull a sled in the Iditarod? is known to be a number, we also need to be aware that this number must quantify the dogs harnessed to a sled in the Iditarod games and not the number of participants in the games.</Paragraph>
    <Paragraph position="1"> Capturing question or answer dependencies can be cast as a straightforward process of mapping syntactic trees to sets of binary head-modifier relationships, as first noted in (Collins, 1996). Given a parse tree, the head-child of each syntactic constituent can be identified based on a simple set of rules used to train syntactic parsers, cf. (Collins, 1996). Dependency relations are established between each leaf corresponding to the head child and the leaves of its constituent sib- null lings that are not stop words, as illustrated by the mapping of Figure 1(a) into Figure 1(b). Unlike in IR systems, question stems are considered content words. When question dependencies are known (Harabagiu et al., 2000) proposed a technique of identifying the answer type based on the semantic category of the question stem and eventually of its most connected dependent concept. For example, in the case of question ET1, illustrated in Figure 1, the answer type is determined by the ambiguous question stem what and the verb visit. The answer type is the object of the verb visit, which is a place of attraction or entertainment, defined by the semantic category LANDMARK. The answer type replaces the question stem, generating the following dependency graph, that can be later unified with the answer dependency graph: mostLANDMARK tourists visit Reims However syntactic dependencies vary across question reformulations or equivalent answers made possible by the productive nature of natural language. For example, the dependency structure of ET2, a reformulation of question ET1 differs from the dependency structure of ET1: Due to the fact that verbs see and visit are synonyms (cf. WordNet (Miller, 1995)) and pronoun I can be read a possible visitor, the dependency Question ET2:</Paragraph>
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