Resolving this-issue anaphora
Published in In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2012
Recommended citation: Varada Kolhatkar and Graeme Hirst. (2009). Resolving "this-issue" anaphora. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. pages 1255 -- 1265, Jeju Island, Korea, July. Association for Computational Linguistics. http://www.aclweb.org/anthology/D12-1115
We annotate and resolve a particular case of abstract anaphora, namely, this-issue anaphora. We propose a candidate ranking model for this-issue anaphora resolution that explores different issue specific and general abstract-anaphora features. The model is not restricted to nominal or verbal antecedents; rather, it is able to identify antecedents that are arbitrary spans of text. Our results show that (a) the model outperforms the strong adjacent-sentence baseline; (b) general abstract-anaphora features, as distinguished from issue-specific features, play a crucial role in this-issue anaphora resolution, suggesting that our approach can be generalized for other NPs such as this problem and this debate; and (c) it is possible to reduce the search space in order to improve performance.