Introduction to the Special Issue on Computational Anaphora Resolution

Abstract

Anaphora accounts for cohesion in texts and is a phenomenon under active study in formal and computational linguistics alike. The correct interpretation of anaphora is vital for natural language processing (NLP). For example, anaphora resolution is a key task in natural language interfaces, machine translation, text summarization, information extraction, question answering, and a number of other NLP applications. After considerable initial research, followed by years of relative silence in the early 1980s, anaphora resolution has attracted the attention of many researchers in the last 10 years and a great deal of successful work on the topic has been carried out. Discourseoriented theories and formalisms such as Discourse Representation Theory and Centering Theory inspired new research on the computational treatment of anaphora. The drive toward corpus-based robust NLP solutions further stimulated interest in alternative and/or data-enriched approaches. Last, but not least, application-driven research in areas such as automatic abstracting and information extraction independently highlighted the importance of anaphora and coreference resolution, boosting research in this area. Much of the earlier work in anaphora resolution heavily exploited domain and linguistic knowledge (Sidner 1979; Carter 1987; Rich and LuperFoy 1988; Carbonell and Brown 1988), which was difficult both to represent and to process, and which required considerable human input. However, the pressing need for the development of robust and inexpensive solutions to meet the demands of practical NLP systems encouraged many researchers to move away from extensive domain and linguistic knowledge and to embark instead upon knowledge-poor anaphora resolution strategies. A number of proposals in the 1990s deliberately limited the extent to which they relied on domain and/or linguistic knowledge and reported promising results in knowledge-poor operational environments (Dagan and Itai 1990, 1991; Lappin and Leass 1994; Nasukawa 1994; Kennedy and Boguraev 1996; Williams, Harvey, and Preston 1996; Baldwin 1997; Mitkov 1996, 1998b). The drive toward knowledge-poor and robust approaches was further motivated by the emergence of cheaper and more reliable corpus-based NLP tools such as partof-speech taggers and shallow parsers, alongside the increasing availability of corpora and other NLP resources (e.g., ontologies). In fact, the availability of corpora, both raw and annotated with coreferential links, provided a strong impetus to anaphora resolu