Acquiring the Meaning of Discourse Markers

Acquiring the Meaning of Discourse Markers Ben Hutchinson School of Informatics University of Edinburgh B.Hutchinson@sms.ed.ac.uk Abstract This paper applies machine learning techniques to acquiring aspects of the meaning of discourse mark- ers. Three subtasks of acquiring the meaning of a discourse marker are considered: learning its polar- ity, veridicality, and type (i.e. causal, temporal or additive). Accuracy of over 90% is achieved for all three tasks, well above the baselines. 1 Introduction This paper is concerned with automatically acquir- ing the meaning of discourse markers. By con- sidering the distributions of individual tokens of discourse markers, we classify discourse markers along three dimensions upon which there is substan- tial agreement in the literature: polarity, veridical- ity and type. This approach of classifying linguistic types by the distribution of linguistic tokens makes this research similar in spirit to that of Baldwin and Bond (2003) and Stevenson and Merlo (1999). Discourse markers signal relations between dis- course units. As such, discourse markers play an important role in the parsing of natural language discourse (Forbes et al., 2001; Marcu, 2000), and their correspondence with discourse relations can be exploited for the unsupervised learning of dis- course relations (Marcu and Echihabi, 2002). In addition, generating natural language discourse re- quires the appropriate selection and placement of discourse markers (Moser and Moore, 1995; Grote and Stede, 1998). It follows that a detailed account of the semantics and pragmatics of discourse mark- ers would be a useful resource for natural language processing. Rather than looking at the finer subtleties in meaning of particular discourse markers (e.g. Best- gen et al. (2003)), this paper aims at a broad scale classification of a subclass of discourse markers: structural connectives. This breadth of coverage is of particular importance for discourse parsing, where a wide range of linguistic realisations must be catered for. This work can be seen as orthogonal to that of Di Eugenio et al. (1997), which addresses the problem of learning if and where discourse markers should be generated. Unfortunately, the manual classification of large numbers of discourse markers has proven to be a difficult task, and no complete classification yet ex- ists. For example, Knott (1996) presents a list of around 350 discourse markers, but his taxonomic classification, perhaps the largest classification in the literature, accounts for only around 150 of these. A general method of automatically classifying dis- course markers would therefore be of great utility, both for English and for languages with fewer man- ually created resources. This paper constitutes a step in that direction. It attempts to classify dis- course markers whose classes are already known, and this allows the classifier to be evaluated empiri- cally. The proposed task of learning automatically the meaning of discourse markers raises several ques- tions which we hope to answer: Q1. Difficulty How hard is it to acquire the mean- ing of discourse markers? Are some aspects of meaning harder to acquire than others? Q2. Choice of features What features are useful for acquiring the meaning of …

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