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<Paper uid="W06-0904">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics A Pilot Study on Acquiring Metric Temporal Constraints for Events</Title>
  <Section position="3" start_page="0" end_page="23" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The growing interest in practical NLP applications such as question-answering and text summarization places increasing demands on the processing of temporal information. In multi-document summarization of news articles, it can be useful to know the relative order of events so as to merge and present information from multiple news sources correctly. In questionanswering, one would like to be able to ask when an event occurs, or what events occurred prior to a particular event. A wealth of prior research by (Passoneau 1988), (Webber 1988), (Hwang and Schubert 1992), (Kamp and Reyle 1993), (Lascarides and Asher 1993), (Hitzeman et al. 1995), (Kehler 2000) and others, has explored the different knowledge sources used in inferring the temporal ordering of events, including temporal adverbials, tense, aspect, rhetorical relations, pragmatic conventions, and background knowledge. For example, the narrative convention of events being described in the order in which they occur is followed in (1), but overridden by means of a discourse relation, Explanation in (2).</Paragraph>
    <Paragraph position="1">  (1) Max stood up. John greeted him.</Paragraph>
    <Paragraph position="2"> (2) Max fell. John pushed him.</Paragraph>
    <Paragraph position="3">  While there has been a spurt of recent research addressing the event ordering problem, e.g., (Mani and Wilson 2000) (Filatova and Hovy 2001) (Schilder and Habel 2001) (Li et al. 2001) (Mani et al. 2003) (Li et al. 2004) (Lapata and Lascarides 2004) (Boguraev and Ando 2005) (Mani et al. 2006), that research relies on qualitative temporal relations. Qualitative relations (e.g., event A BEFORE event B, or event A DURING time T) are certainly of interest in developing timelines of events in news and other genres.</Paragraph>
    <Paragraph position="4"> However, metric constraints can also be potentially useful in this ordering problem. For example, in (3), it can be crucial to know whether the bomb landed a few minutes to hours or several years BEFORE the hospitalization. While humans have strong intuitions about this from commonsense knowledge, machines don't.</Paragraph>
    <Paragraph position="5"> (3) An elderly Catholic man was hospitalized from cuts after a Protestant gasoline bomb landed in his back yard.</Paragraph>
    <Paragraph position="6"> Fortunately, there are numerous instances such as (4), where metric constraints are specified explicitly: null (4) The company announced Tuesday that third quarter sales had fallen.</Paragraph>
    <Paragraph position="7"> In (4), the falling of sales occurred over the three-month period of time inferable from the speech time. However, while the announcement is anchored to a day inferable from the speech  time, the length of the announcement is not specified.</Paragraph>
    <Paragraph position="8"> These examples suggest that it may be possible to mine information from a corpus to fill in extents for the time intervals of and between events, when these are either unspecified or partially specified. Metric constraints can also potentially lead to better qualitative links, e.g., events with long durations are more likely to overlap with other events.</Paragraph>
    <Paragraph position="9"> This paper describes some preliminary experiments to acquire metric constraints. The approach extends the TimeML representation (Pustejovsky et al. 2005) to include such constraints. We first translate a TimeML representation with qualitative relations into one where metric constraints are added. This representation may tell us how long certain events last, and the length of the gaps between them, given the information in the text. However, the information in the text may be incomplete; some extents may be unknown. We therefore need an external source of knowledge regarding the typical extents of events, which we can use when the text doesn't provide it. We accordingly describe an initial attempt to bootstrap event durations from raw corpora as well as corpora annotated with qualitative relations.</Paragraph>
  </Section>
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