Temporal Discourse Models for Narrative Structure

Temporal Discourse Models for Narrative Structure Inderjeet MANI Department of Linguistics Georgetown University ICC 452 Washington, DC 20057 im5@georgetown.edu James PUSTEJOVSKY Department of Computer Science Brandeis University Volen 258 Waltham, Massachusetts 02254 jamesp@cs.brandeis.edu Abstract Getting a machine to understand human narratives has been a classic challenge for NLP and AI. This paper proposes a new representation for the temporal structure of narratives. The representation is parsimonious, using temporal relations as surrogates for discourse relations. The narrative models, called Temporal Discourse Models, are tree- structured, where nodes include abstract events interpreted as pairs of time points and where the dominance relation is expressed by temporal inclusion. Annotation examples and challenges are discussed, along with a report on progress to date in creating annotated corpora. 1 Introduction Getting a machine to understand human narratives has been a classic challenge for NLP and AI. Central to all narratives is the notion of time and the unfolding of events. When we understand a story, in addition to understanding other aspects such as plot, characters, goals, etc., we are able to understand the order of happening of events. A given text may have multiple stories; when we understand such a text, we are able to tease apart these distinct stories. Thus, understanding the story from a text involves building a global model of the sequences of events in the text, as well as the structure of nested stories. We refer to such models as Temporal Discourse Models (TDMs). Currently, while we have informal descriptions of the structure of narratives, e.g., (Bell 1999), we lack a precise understanding of this aspect of discourse. What sorts of structural configurations are observed? What formal characteristics do they have? For syntactic processing of natural languages, we have, arguably, answers to similar questions. However, for discourse, we have hardly begun to ask the questions. One of the problems here is that most of the information about narrative structure is implicit in the text. Thus, while linguistic information in the form of tense, aspect, temporal adverbials and discourse markers is often present, people use commonsense knowledge to fill in information. Consider a simple discourse: Yesterday Holly was running a marathon when she twisted her ankle. David had pushed her. Here, aspectual information indicates that the twisting occurred during the running, while tense suggests that the pushing occurs before the twisting. Commonsense knowledge also suggests that the pushing caused the twisting. We can see that even for interpreting such relatively simple discourses, a system might require a variety of sources of linguistic knowledge, including knowledge of tense, aspect, temporal adverbials, discourse relations, as well as background knowledge. Of course, other inferences are clearly possible, e.g., that the running stopped after the twisting, but when viewed as defaults, these latter inferences seem to be more easily violated. The need for commonsense inferences has motivated computational approaches that are domain- specific, using hand-coded knowledge (e.g., Asher and Lascarides 2003, Hitzeman et al. 1995). A number of theories have postulated the existence of various discourse …

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