Sentence Level Discourse Parsing using Syntactic and Lexical Information

Sentence Level Discourse Parsing using Syntactic and Lexical Information Radu Soricut and Daniel Marcu Information Sciences Institute University of Southern California 4676 Admiralty Way, Suite 1001 Marina del Rey, CA 90292 � radu, marcu � @isi.edu Abstract We introduce two probabilistic models that can be used to identify elementary discourse units and build sentence-level discourse parse trees. The models use syntactic and lexical features. A discourse parsing algorithm that implements these models derives discourse parse trees with an error reduction of 18.8% over a state-of- the-art decision-based discourse parser. A set of empirical evaluations shows that our dis- course parsing model is sophisticated enough to yield discourse trees at an accuracy level that matches near-human levels of performance. 1 Introduction By exploiting information encoded in human-produced syntactic trees (Marcus et al., 1993), research on prob- abilistic models of syntax has driven the performance of syntactic parsers to about 90% accuracy (Charniak, 2000; Collins, 2000). The absence of semantic and discourse annotated corpora prevented similar developments in se- mantic/discourse parsing. Fortunately, recent annotation projects have taken significant steps towards developing semantic (Fillmore et al., 2002; Kingsbury and Palmer, 2002) and discourse (Carlson et al., 2003) annotated cor- pora. Some of these annotation efforts have already had a computational impact. For example, Gildea and Juraf- sky (2002) developed statistical models for automatically inducing semantic roles. In this paper, we describe proba- bilistic models and algorithms that exploit the discourse- annotated corpus produced by Carlson et al. (2003). A discourse structure is a tree whose leaves correspond to elementary discourse units (edu)s, and whose internal nodes correspond to contiguous text spans (called dis- course spans). An example of a discourse structure is the tree given in Figure 1. Each internal node in a dis- course tree is characterized by a rhetorical relation, such it will use its network The bank also says 1 2 3 [2,3] ATTRIBUTION to channel investments. [ ] [ ] [ ] 1 2 3 ENABLEMENT Figure 1: Discourse structure of a sentence. as ATTRIBUTION and ENABLEMENT. Within a rhetorical re- lation a discourse span is also labeled as either NUCLEUS or SATELLITE. The distinction between nuclei and satel- lites comes from the empirical observation that a nucleus expresses what is more essential to the writer’s purpose than a satellite. Discourse trees can be represented graph- ically in the style shown in Figure 1. The arrows link the satellite to the nucleus of a rhetorical relation. Arrows are labeled with the name of the rhetorical relation that holds between the linked units. Horizontal lines correspond to text spans, and vertical lines identify text spans which are nuclei. In this paper, we introduce two probabilistic models that can be used to identify elementary discourse units and build sentence-level discourse parse trees. We show how syntactic and lexical information can be exploited in the process of identifying elementary units of discourse and building sentence-level discourse trees. Our evalu- ation indicates that the discourse parsing model we pro- pose is sophisticated enough …

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