Attention, intentions, and the structure of discourse
Computational Linguistics
Centering: a framework for modeling the local coherence of discourse
Computational Linguistics
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Evaluation of text coherence for electronic essay scoring systems
Natural Language Engineering
A centering approach to pronouns
ACL '87 Proceedings of the 25th annual meeting on Association for Computational Linguistics
Providing a unified account of definite noun phrases in discourse
ACL '83 Proceedings of the 21st annual meeting on Association for Computational Linguistics
Context-based question-answering evaluation
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
IQA '06 Proceedings of the Interactive Question Answering Workshop at HLT-NAACL 2006
Answering contextual questions based on ontologies and question templates
Frontiers of Computer Science in China
| Hi-index | 0.00 |
Question answering (QA) systems take users' natural language questions and retrieve relevant answers from large repositories of free texts. Despite recent progress in QA research, most work on question answering is still focused on isolated questions. In a real-world information seeking scenario, questions are not asked in isolation, but rather in a coherent manner that involves a sequence of related questions to meet users' information needs. Therefore, to support coherent information seeking, intelligent QA interfaces will inevitably require techniques to support context question answering. To address this problem, this paper investigates approaches to discourse processing of a sequence of coherent questions and their implications on query expansion. In particular, we examine three models for query expansion that are motivated by Centering Theory. 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.