Entity-Based Local Coherence Modelling Using Topological Fields

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 186–195, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Entity-based local coherence modelling using topological fields Jackie Chi Kit Cheung and Gerald Penn Department of Computer Science University of Toronto Toronto, ON, M5S 3G4, Canada {jcheung,gpenn}@cs.toronto.edu Abstract One goal of natural language generation is to produce coherent text that presents in- formation in a logical order. In this pa- per, we show that topological fields, which model high-level clausal structure, are an important component of local coherence in German. First, we show in a sen- tence ordering experiment that topologi- cal field information improves the entity grid model of Barzilay and Lapata (2008) more than grammatical role and simple clausal order information do, particularly when manual annotations of this informa- tion are not available. Then, we incor- porate the model enhanced with topolog- ical fields into a natural language gen- eration system that generates constituent orders for German text, and show that the added coherence component improves performance slightly, though not statisti- cally significantly. 1 Introduction One type of coherence modelling that has captured recent research interest is local coherence mod- elling, which measures the coherence of a docu- ment by examining the similarity between neigh- bouring text spans. The entity-based approach, in particular, considers the occurrences of noun phrase entities in a document (Barzilay and Lap- ata, 2008). Local coherence modelling has been shown to be useful for tasks like natural language generation and summarization, (Barzilay and Lee, 2004) and genre classification (Barzilay and Lap- ata, 2008). Previous work on English, a language with rel- atively fixed word order, has identified factors that contribute to local coherence, such as the gram- matical roles associated with the entities. There is good reason to believe that the importance of these factors vary across languages. For instance, freer- word-order languages exhibit word order patterns which are dependent on discourse factors relating to information structure, in addition to the gram- matical roles of nominal arguments of the main verb. We thus expect word order information to be particularly important in these languages in dis- course analysis, which includes coherence mod- elling. For example, Strube and Hahn (1999) introduce Functional Centering, a variant of Centering The- ory which utilizes information status distinctions between hearer-old and hearer-new entities. They apply their model to pronominal anaphora reso- lution, identifying potential antecedents of sub- sequent anaphora by considering syntactic and word order information, classifying constituents by their familiarity to the reader. They find that their approach correctly resolves more pronomi- nal anaphora than a grammatical role-based ap- proach which ignores word order, and the differ- ence between the two approaches is larger in Ger- man corpora than in English ones. Unfortunately, their criteria for ranking potential antecedents re- quire complex syntactic information in order to classify whether proper names are known to the hearer, which makes their algorithm hard to auto- mate. Indeed, all evaluation is done manually. We instead use topological …

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