Supervised Noun Phrase Coreference Research: The First Fifteen Years

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1396–1411, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Supervised Noun Phrase Coreference Research: The First Fifteen Years Vincent Ng Human Language Technology Research Institute University of Texas at Dallas Richardson, TX 75083-0688 vince@hlt.utdallas.edu Abstract The research focus of computational coreference resolution has exhibited a shift from heuristic approaches to machine learning approaches in the past decade. This paper surveys the major milestones in supervised coreference research since its inception fifteen years ago. 1 Introduction Noun phrase (NP) coreference resolution, the task of determining which NPs in a text or dialogue re- fer to the same real-world entity, has been at the core of natural language processing (NLP) since the 1960s. NP coreference is related to the task of anaphora resolution, whose goal is to identify an antecedent for an anaphoric NP (i.e., an NP that depends on another NP, specifically its an- tecedent, for its interpretation) [see van Deemter and Kibble (2000) for a detailed discussion of the difference between the two tasks]. Despite its sim- ple task definition, coreference is generally con- sidered a difficult NLP task, typically involving the use of sophisticated knowledge sources and inference procedures (Charniak, 1972). Compu- tational theories of discourse, in particular focus- ing (see Grosz (1977) and Sidner (1979)) and cen- tering (Grosz et al. (1983; 1995)), have heavily influenced coreference research in the 1970s and 1980s, leading to the development of numerous centering algorithms (see Walker et al. (1998)). The focus of coreference research underwent a gradual shift from heuristic approaches to machine learning approaches in the 1990s. This shift can be attributed in part to the advent of the statisti- cal NLP era, and in part to the public availability of annotated coreference corpora produced as part of the MUC-6 (1995) and MUC-7 (1998) confer- ences. Learning-based coreference research has remained vibrant since then, with results regularly published not only in general NLP conferences, but also in specialized conferences (e.g., the bien- nial Discourse Anaphora and Anaphor Resolution Colloquium (DAARC)) and workshops (e.g., the series of Bergen Workshop on Anaphora Resolu- tion (WAR)). Being inherently a clustering task, coreference has also received a lot of attention in the machine learning community. Fifteen years have passed since the first paper on learning-based coreference resolution was pub- lished (Connolly et al., 1994). Our goal in this paper is to provide NLP researchers with a sur- vey of the major milestones in supervised coref- erence research, focusing on the computational models, the linguistic features, the annotated cor- pora, and the evaluation metrics that were devel- oped in the past fifteen years. Note that several leading coreference researchers have published books (e.g., Mitkov (2002)), written survey arti- cles (e.g., Mitkov (1999), Strube (2009)), and de- livered tutorials (e.g., Strube (2002), Ponzetto and Poesio (2009)) that provide a broad overview of coreference research. This survey paper aims to complement, rather than supersede, these previ- ously published materials. In particular, while …

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