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<Paper uid="W04-2505">
  <Title>Intentions, Implicatures and Processing of Complex Questions</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The Problem of Question Intentions.</Paragraph>
    <Paragraph position="1"> When using a Question Answering system to find information, the user cannot separate the intentions and beliefs from the formulation of the question. A direct consequence of this phenomenon is that the user incorporates his or her intentions and beliefs into the interrogation. For example, when asking the question: Q1: What kind of assistance has North Korea received from the USSR/Russia for its missile program? the user associate with the question a number of intentions, that maybe expressed a set of intended questions. Each intended question, in turn generates implied information, that maybe expresses as implied questions.</Paragraph>
    <Paragraph position="2"> For question Q1, a list of intended questions and implied questions is detailed in Table1.</Paragraph>
    <Paragraph position="3"> Most of the intended questions are similar with the questions evaluated in TREC1. For example questions  workshops in which Information Retrieval tasks are annually tested. Since 1999 the performance of question answering systems are measured in the TREC QA track.</Paragraph>
    <Paragraph position="4"> Qi1, Qi2 and Qi3 are so-called definition questions, since they ask about defining properties of an object. However unlike the TREC definition questions, these questions express unstated intentions of the questioner and need to be processed in the context of the original complex question Q1. Questions Qi4 and Qi5 are factoid questions, requesting information about facts or events. Qi6 asks about the source of information that enables the answers of question Q1.</Paragraph>
    <Paragraph position="5"> Questions Qi1, Qi2, Qi3, Qi4 and Qi5 result from the intentional structure generated when processing question Q1 or questions similar to it. When intended questions are generated, their sequential processing (a) represents a decomposition of the complex question and (b) generates a scenario for finding information; thus questions like Q1 are also known as scenario questions.</Paragraph>
    <Paragraph position="6"> Intentions and Implicatures.</Paragraph>
    <Paragraph position="7"> As Table 1 suggests, the implied information takes the form of alternatives that guide the answers to intended questions. For example, question Qm11 lists alternatives for the answer to Qi1 whereas Qm21 lists components of the answer of Qi1. Implicatures may also involve temporal inference, e.g. the implied questions pertaining to Qi3 and Qi4. Additionally, the reliability of information is commonly an implicature in the case of scenario questions, since the causal and temporal inference is based on the quality and correctness of the available data sources. Neither intentions or implicatures are recognizable at syntactic or semantic level, but they both play an important role in the question interpretation. Interpretations disregard the implied information or the user intentions determine the extraction of incorrect answers, thus influence the performance of Q/A systems.</Paragraph>
    <Paragraph position="8"> Our solution.</Paragraph>
    <Paragraph position="9"> In this paper we present two different mechanisms of deriving the question implicatures. Both methods start from the syntactic and semantic content of the interro- null meaning of the words used in the question whereas the second method considers the predicate-argument structure of the question and candidate answers as a form of shallow semantics that enables the inference of the intentional structure. Question implicatures are derived from lexico-semantic paths retrieved from the WordNet lexico-semantic database. These paths bring forward new concepts, that may be associated with the question implicatures when testing the paths against the conversational maxims introduced by Grice in (Grice, 1975a). For example, if the user asks &amp;quot;Will Prime Minister Mori survive the crisis?&amp;quot;, the first method detects the user's belief that the position of the Prime Minister is in jeopardy, since the concept DANGER is coerced although none of the question words directly imply it.</Paragraph>
    <Paragraph position="10"> The second method generates the intentional structure of the question, enabling a more structured representation of the pragmatics of question interpretation. The intentional structure is based on a study that we have conducted for capturing the motivations of a group of users when asking series of questions in several scenarios. We show how the intentional structures that we have gathered guide the coercion of knowledge that helps to support the acceptance of rejection of computational implicatures.</Paragraph>
    <Paragraph position="11"> The derivation of intentional structures is made possible by predicate-argument structures that are recognized both at the question level and at the candidate answer level. In this paper we show how richer semantic objects can be derived around predicate-argument structures and how inferential mechanisms can be associated with such semantic objects for obtaining correct answers. The rest of the paper is organized as follows. In Section 2 we describe several forms of complex questions that require the derivation of computational implicatures. Section 3 details the models of Question Answering that we considered and Section 4 shows our methods of deriving predicate-argument structures and their usage in identifying answers for questions. Section 5 details the intentional structures whereas Section 6 summarizes the conclusions. null</Paragraph>
  </Section>
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