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<Paper uid="W03-1210">
  <Title>Automatic Detection of Causal Relations for Question Answering</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
2 Previous Work in Computational
Linguistics
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    <Paragraph position="0"> Computational linguists have tried to tackle the notion of causality in natural language focusing on lexical and semantic constructions that can express this relation.</Paragraph>
    <Paragraph position="1"> Many previous studies have attempted to extract implicit inter-sentential cause-effect relations from text using knowledge-based inferences (Joskowiscz et al. 1989), (Kaplan 1991). These studies were based on hand-coded, domain-specific knowledge bases difficult to scale up for realistic applications. More recently, other researchers (Garcia 1997), (Khoo et al. 2000) used linguistic patterns to identify explicit causation relations in text without any knowledge-based inference. Garcia used French texts to capture causation relationships through linguistic indicators organized in a semantic model which classifies causative verbal patterns. She found 25 causal relations with an approach based on the &amp;quot;Force Dynamics&amp;quot; of Leonard Talmy claiming a precision of 85%.</Paragraph>
    <Paragraph position="2"> Khoo at al. used predefined verbal linguistic patterns to extract cause-effect information from business and medical newspaper texts. They presented a simple computational method based on a set of partially parsed linguistic patterns that usually indicate the presence of a causal relationship. The relationships were determined by exact matching on text with a precision of about 68%.</Paragraph>
    <Paragraph position="3"> 3 How are causation relations expressed in English? Any causative construction involves two components, the cause and its effect. For example: &amp;quot;The bus fails to turn up. As a result, I am late for a meeting&amp;quot;.(Comrie 1981) Here the cause is represented by the bus's failing to turn up, and the effect by my being late for the meeting.</Paragraph>
    <Paragraph position="4"> In English, the causative constructions can be explicit or implicit. Usually, explicit causation patterns can contain relevant keywords such as cause, effect, consequence, but also ambiguous ones such as generate, induce, etc. The implicit causative constructions are more complex, involving inference based on semantic analysis and background knowledge. The English language provides a multitude of cause-effect expressions that are very productive. In this paper we focus on explicit but ambiguous verbal causation patterns and provide a detailed computational analysis. A list of other causation expressions were presented in detail elsewhere (Girju 2002).</Paragraph>
    <Paragraph position="5"> Causation verbs Many linguists focused their attention on causative verbal constructions that can be classified based on a lexical decomposition. This decomposition builds a taxonomy of causative verbs according to whether they define only the causal link or the causal link  plus other components of the two entities that are causally related (Nedjalkov and Silnickij 1969): 1. Simple causatives (cause, lead to, bring about, generate, make, force, allow, etc.) Here the linking verb refers only to the causal link, being synonymous with the verb cause. E.g., &amp;quot;Earthquakes generatetidal waves.&amp;quot; 2. Resultative causatives (kill, melt, dry, etc.) These verbs refer to the causal link plus a part of the resulting situation.</Paragraph>
    <Paragraph position="6"> 3. Instrumental causatives (poison (killing by poi null soning), hang, punch, clean, etc.) These causatives express a part of the causing event as well as the result.</Paragraph>
  </Section>
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