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Towards a Computational Theory of Definite Anaphora Comprehension in English Discourse
Towards a Computational Theory of Definite Anaphora Comprehension in English Discourse
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Natural Language Engineering
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ACL '83 Proceedings of the 21st annual meeting on Association for Computational Linguistics
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MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
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Speech and Language Processing (2nd Edition)
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AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
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SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Knowledge-Based Systems
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CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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Motivated by the recent effort on scenario-based context question answering (QA), this paper investigates the role of discourse processing and its implication on query expansion for a sequence of questions. Our view is that a question sequence is not random, but rather follows a coherent manner to serve some information goals. Therefore, this sequence of questions can be considered as a mini discourse with some characteristics of discourse cohesion. Understanding such a discourse will help QA systems better interpret questions and retrieve answers. Thus, we examine three models driven by Centering Theory for discourse processing: a reference model that resolves pronoun references for each question, a forward model that makes use of the forward looking centers from previous questions, and a transition model that takes into account the transition state between adjacent questions. Our empirical results indicate that more sophisticated processing based on discourse transitions and centers can significantly improve the performance of document retrieval compared to models that only resolve references. This paper provides a systematic evaluation of these models and discusses their potentials and limitations in processing coherent context questions.