Machine-Assisted Rhetorical Structure Annotation

Machine-Assisted Rhetorical Structure Annotation Manfred Stede and Silvan Heintze University of Potsdam Dept. of Linguistics Applied Computational Linguistics D-14415 Potsdam Germany stede|heintze@ling.uni-potsdam.de Abstract Manually annotating the rhetorical struc- ture of texts is very labour-intensive. At the same time, high-quality automatic analysis is currently out of reach. We thus propose to split the manual annotation in two phases: the simpler marking of lexical connectives and their relations, and the more difficult decisions on overall tree structure. To this end, we developed an environment of two analysis tools and XML-based declarative resources. Our ConAno tool allows for effi- cient, interactive annotation of connectives, scopes and relations. This intermediate re- sult is exported to O’Donnell’s ‘RST Tool’, which facilitates completing the tree struc- ture. 1 Introduction A number of approaches tackling the difficult problem of automatic discourse parsing have been proposed in recent years (e.g., (Sumita et al., 1992) (Marcu, 1997), (Schilder, 2002)). They differ in their orientation toward sym- bolic or statistical information, but they all — quite naturally — share the assumption that the lexical connectives or discourse markers are the primary source of information for construct- ing a rhetorical tree automatically. The den- sity of discourse markers in a text depends on its genre (e.g., commentaries tend to have more than narratives), but in general, it is clear that only a portion of the relations holding in a text is lexically signalled.1 Furthermore, it is well- known that discourse markers are often ambigu- ous; for example, the English but can, in terms of (Mann, Thompson, 1988), signal any of the relations Antithesis, Contrast, and Concession. Accordingly, automatic discourse parsing focus- ing on connectives is bound to have its limita- tions. 1In our corpus of newspaper commentaries (Stede, 2004), we found that 35% of the coherence relations are signalled by a connective. Our position is that progress in discourse parsing relies on the one hand on a more thor- ough understanding of the underlying issues, and on the other hand on the availability of human-annotated corpora, which can serve as a resource for in-depth studies of discourse- structural phenomena, and also for training statistical analysis programs. Two examples of such corpora are the RST Tree Corpus by (Marcu et al., 1999) for English and the Pots- dam Commentary Corpus (Stede, 2004) for German. Producing such resources is a labour- intensive task that requires time, trained anno- tators, and clearly specified guidelines on what relation to choose under which circumstances. Nonetheless, rhetorical analysis remains to be in part a rather subjective process (see section 2). In order to eventually arrive at more objec- tive, comparable results, our proposal is to split the annotation process into two parts: 1. Annotation of connectives, their scopes (the two related textual units), and — op- tionally — the signalled relation 2. Annotation of the remaining (unsignalled) relations between larger segments Step 1 is inspired by work done for English in the Penn Discourse TreeBank2 (Miltsakaki et al., 2004). In our two-step scenario, it is the easier …

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