A Language-Independent Anaphora Resolution System for Understanding Multilingual Texts

A LANGUAGE-INDEPENDENT ANAPHORA RES()LUTION SYSTEM FOR UNDERSTANDING MULTILINGUAL TEXTS Chinatsu Aone and Douglas McKee Systems Research and Applications (SRA) 2000 15th Street North Arlington, VA 22201 aonec@sra.com, mckeed@sra.com Abstract This paper describes a new discourse module within our multilingual NLP system. Because of its unique data-driven architecture, the discourse module is language-independent. Moreover, the use of hierarchically organized multiple knowledge sources makes the module robust and trainable using discourse-tagged corpora. Separating discourse phe- nomena from knowledge sources makes the discourse module easily extensible to additional phenomena. 1 Introduction This paper describes a new discourse module within our multilingual natural language processing system which has been used for understanding texts in En- glish, Spanish and Japanese (el. [1, 2])) The follow- ing design principles underlie the discourse module: • Language-independence: No processing code de- pends on language-dependent facts. • Extensibility: It is easy to handle additional phe- nomena. • Robustness: The discourse module does its best even when its input is incomplete or wrong. • Trainability: The performance can be tuned for particular domains and applications. In the following, we first describe the architecture of the discourse module. Then, we discuss how its performance is evaluated and trained using discourse- tagged corpora. Finally, we compare our approach to other research. 1 Our system has been used in several data extraction tasks and a prototype nlachine translation systeln. perfo.m .... ~nti ~u2k c$~ " e dv . . . . . . . . . . . . . . . . . . . . . . . . . . r . . . . . . . . . . . . . . . . . . . . o . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . -, l:)i~ ~ Module Figure 1: Discourse Architecture 2 Discourse Architecture Our discourse module consists of two discourse pro- cessing submodules (the Discourse A dministralor and the Resolution Engine), and three discourse knowl- edge bases (the Discourse Knowledge Source KB, the Discourse Phenomenon KB, and the Discourse Domain KB). The Discourse Administrator is a development-time tool for defining the three dis- course KB's. The Resolution Engine, on the other hand, is the run-time processing module which ac- tually performs anaphora resolution using these dis- course KB's. The Resolution Engine also has access to an ex- ternal discourse data structure called the global dis- course world, which is created by the top-level text processing controller. The global discourse world holds syntactic, semantic, rhetorical, and other infor- mation about the input text derived by other parts of the system. The architecture is shown in Figure i. 2.1 Discourse Data Structures There are four major discourse data types within the global discourse world: Discourse World (DW), [)is- 156 course Clause (DC), Discourse Marker (DM), and File Card (FC), as shown in Figure 2. The …

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