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<Paper uid="W01-1315">
  <Title>The Annotation of Temporal Information in Natural Language Sentences</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> In interpreting narratives the most essential information to be extracted is who did what where, when and why, the classic journalistic imperatives. The 'who' and 'what' information is usually expressed overtly, and this has made it possible to apply empirical techniques to problems in this domain (such as word-sense classification and argument structure mapping).</Paragraph>
    <Paragraph position="1"> The 'when' and 'where' information is, however, often left implicit, or, at least, only partially specified, making it difficlut to apply such techniques to this domain.</Paragraph>
    <Paragraph position="2"> Formal semantic theories of temporal interpretation (e.g. Kamp &amp; Reyle 1993; Ogihara 1996; Abusch 1997) have been quite successful at specifying the contribution that such overt markers as tenses and temporal adverbials make to the meaning of a sentence or discourse. Investigations into the interpretation of narrative discourse (Lascarides &amp; Asher 1993, Reyle &amp; Rossdeutscher 2000) have, however, shown that very specific lexical information plays an important role in determining temporal interpretation. As of yet it is not clear how this kind of lexical information could be automatically acquired. The most promising avenue for acquiring lexical information appears to be automatic induction from very large annotated corpora (Rooth, et. al. 1998). In order to apply these techniques to the temporal domain it is necessary that the temporal information be made explicit. Our task here is to provide a system of temporal annotation that fulfills this requirement.</Paragraph>
    <Paragraph position="3"> The systems for temporal annotation we are familiar with have been concerned either with absolute temporal information (Wiebe, et. al.</Paragraph>
    <Paragraph position="4"> 1998, Androutsopoulos, Rithie &amp; Thanisch 1997), or with the annotation of overt markers (Setzer &amp; Gaizauskas 2000). Much temporal information, however, is not absolute but relative and not overtly marked but implicit. We are frequently only interested (and only have information about) the order events occurred in. And while there are sometimes overt markers for these temporal relations, the conjunctions before, after and when being the most obvious, usually this kind of relational information is implicit. The examples in (1) illustrate the phenomenon.</Paragraph>
    <Paragraph position="5">  (1) a. John kissed the girl he met at the party. b. Leaving the party, John walked home.</Paragraph>
    <Paragraph position="6"> c. He remembered talking to her and asking  her for her name.</Paragraph>
    <Paragraph position="7"> Although there are no obvious markers for the temporal ordering of the events described in these sentences, native speakers have clear intuitions about what happened when: we know that the kissing took place after the meeting and that the asking was part of the talking. But how do we know this? And - more importantly how could this information be automatically extracted from these sentences? These are the questions that motivate the development of our annotation system.</Paragraph>
    <Paragraph position="8"> We believe that the creation of a large scale treebank annotated with relational temporal information as well as standard morphological and syntactic information will make it possible to investigate these issues productively. The annotated treebank must be large scale for the obvious reason that the application of stochastic methods requires this. It should be syntactically as well as semantically annotated because we consider it quite likely that syntactic as well as lexical information plays a role in temporal interpretation. We don't know a priori whether in (1a) it is the lexical relationship between kiss and meet that is crucial to determining the temporal interpretation, or whether the fact that meet is in a subordinate clause - the syntactic relation - also plays a role. To answer these kinds of questions it is necessary to encode the temporal information conveyed by a sentence in a way which makes addressing such questions possible.</Paragraph>
    <Paragraph position="9"> What we describe below is a practical system for encoding relational temporal information that is suited to large-scale hand annotation of texts. This system has a number of applications beyond this, both in the domain of cross-linguistic investigation and in empirical NLP.</Paragraph>
  </Section>
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