1 Anaphora Resolution : Where Do We Stand Now ?

This paper argues that even though there has been considerable advance in the research in anaphora resolution over the last 10 years, there are still a number of outstanding issues. The paper discusses several of these issues and outlines some of the work underway to address them with particular reference to the work carried out by the author’s research team. 1 Anaphora Resolution: Where Do We Stand Now? Anaphora accounts for cohesion in text and is a phenomenon of active study in formal and computational linguistics alike. The correct interpretation of anaphora is vital for Natural Language Processing. For example, anaphora resolution is a key task in natural language interfaces, machine translation, automatic abstracting, information extraction, and in a number of other NLP applications. After considerable initial research, and after years of relative silence in the early eighties, anaphora resolution attracted the attention of many researchers in the last 10 years and much promising work on the topic has been reported. Discourse-orientated theories and formalisms such as DRT and Centering have inspired new research on the computational treatment of anaphora. The drive towards corpus-based robust NLP solutions has further stimulated interest, for alternative and/or data-enriched approaches. Last but not least, application-driven research in areas such as automatic abstracting and information extraction has independently identified the importance of (and boosted the research in) anaphora and coreference resolution. Much of the earlier work in anaphora resolution heavily exploited domain and linguistic knowledge ([9], [11], [57], [58]), which was difficult both to represent and process, and 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 rely on domain and/or linguistic knowledge ([6], [15], [31], [35], [38], [51], [65]) and reported promising results in knowledge-poor operational environments. The drive towards knowledge-poor and robust approaches was further motivated by the emergence of cheaper and more reliable corpus-based NLP tools such as POS taggers and shallow parsers, alongside the increasing availability of corpora and other Outstanding Issues in Anaphora Resolution 111 NLP resources (e.g. ontologies). In fact the availability of corpora, both raw and annotated with coreferential links, provided a strong impetus to anaphora resolution with regard to both training and evaluation. Corpora (especially when annotated) are an invaluable source not only for empirical research but also for automated learning methods (e.g. Machine Learning methods) aiming to develop new rules and approaches, and provide also an important resource for evaluation of the implemented approaches. From simple co-occurrence rules ([15]) through training decision trees to identify anaphor-antecedent pairs ([3]) to genetic algorithms to optimise the resolution factors ([53]), the successful performance of more and more modern approaches was made possible through the availability of suitable corpora. Whereas the last 10 years have seen considerable advances in the field of anaphora resolution, there are still a number of outstanding issues that remain either unsolved or need further attention and, as a consequence, represent major challenges to the further development of the field. One significant problem for automatic anaphora resolution systems is that the accuracy of the pre-processing is still too low and as a consequence the performance of such systems is still far from ideal. As a further consequence, only a few anaphora resolution systems operate in fully automatic mode: most of them rely or manual pre-processing or use pre-analysed corpora. One of the impediments for the evaluation or for the employment of Machine Learning (ML) techniques is the lack of widely available corpora annotated for anaphoric or coreferential links. More research into the factors influencing the performance of the resolution algorithm is necessary; so too is work towards the proposal of consistent and comprehensive evaluation. This paper discusses some of the outstanding issues in anaphora resolution and outlines some of the work underway to address them with particular reference to the work carried out by the author’s research team.1 The paper covers the task of anaphora resolution and not that of coreference resolution even though some of the issues raised apply to both tasks. In anaphora resolution the system has to determine the antecedent of the anaphor; for identity-of-reference nominal anaphora any preceding NP which is coreferential with the anaphor, is considered as the correct antecedent. On the other hand, the objective of coreference resolution is to identify all coreferential chains. 2 Pre-processing and Dully Automatic Anaphora Resolution A real-world anaphora resolution system vitally depends on the efficiency of the preprocessing tools that analyse the input before feeding it to the resolution algorithm. Inaccurate pre-processing could lead to a considerable drop in the performance of the system, however accurate an anaphora resolution algorithm may be. In the preprocessing stage a number of hard pre-processing problems such as morphological analysis / POS tagging, named entity recognition, unknown word recognition, NP extraction, parsing, identification of pleonastic pronouns, selectional constraints, etc. have to be dealt with. Each one of these tasks introduces error and thus contributes to a reduction of the success rate of the anaphora resolution system. The accuracy of 1 Research Group in Computational Linguistics, School of Humanities, Languages and European Studies, University of Wolverhampton (http://www.wlv.ac.uk/sles/compling/).