An Unsupervised Approach to Recognizing Discourse Relations

An Unsupervised Approach to Recognizing Discourse Relations Daniel Marcu and Abdessamad Echihabi Information Sciences Institute and Department of Computer Science University of Southern California 4676 Admiralty Way, Suite 1001 Marina del Rey, CA, 90292 � marcu,echihabi � @isi.edu Abstract We present an unsupervised approach to recognizing discourse relations of CON- TRAST, EXPLANATION-EVIDENCE, CON- DITION and ELABORATION that hold be- tween arbitrary spans of texts. We show that discourse relation classifiers trained on examples that are automatically ex- tracted from massive amounts of text can be used to distinguish between some of these relations with accuracies as high as 93%, even when the relations are not ex- plicitly marked by cue phrases. 1 Introduction In the field of discourse research, it is now widely agreed that sentences/clauses are usually not un- derstood in isolation, but in relation to other sen- tences/clauses. Given the high level of interest in explaining the nature of these relations and in pro- viding definitions for them (Mann and Thompson, 1988; Hobbs, 1990; Martin, 1992; Lascarides and Asher, 1993; Hovy and Maier, 1993; Knott and Sanders, 1998), it is surprising that there are no ro- bust programs capable of identifying discourse rela- tions that hold between arbitrary spans of text. Con- sider, for example, the sentence/clause pairs below. a. Such standards would preclude arms sales to states like Libya, which is also currently sub- ject to a U.N. embargo. b. But states like Rwanda before its present crisis would still be able to legally buy arms. (1) a. South Africa can afford to forgo sales of guns and grenades b. because it actually makes most of its profits from the sale of expensive, high-technology systems like laser-designated missiles, air- craft electronic warfare systems, tactical ra- dios, anti-radiation bombs and battlefield mo- bility systems. (2) In these examples, the discourse markers But and because help us figure out that a CONTRAST re- lation holds between the text spans in (1) and an EXPLANATION-EVIDENCE relation holds between the spans in (2). Unfortunately, cue phrases do not signal all relations in a text. In the corpus of Rhetori- cal Structure trees (www.isi.edu/ � marcu/discourse/) built by Carlson et al. (2001), for example, we have observed that only 61 of 238 CONTRAST relations and 79 out of 307 EXPLANATION-EVIDENCE rela- tions that hold between two adjacent clauses were marked by a cue phrase. So what shall we do when no discourse markers are used? If we had access to ro- bust semantic interpreters, we could, for example, infer from sentence 1.a that “can- not buy arms legally(libya)”, infer from sen- tence 1.b that “can buy arms legally(rwanda)”, use our background knowledge in order to infer that “similar(libya,rwanda)”, and apply Hobbs’s (1990) definitions of discourse relations to arrive at the conclusion that a CONTRAST relation holds between the sentences in (1). Unfortunately, the state of the art in NLP does not provide us access to semantic interpreters and general purpose knowledge bases that would support these kinds of inferences. The discourse relation …

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