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Computational Linguistics
Kernel methods for relation extraction
The Journal of Machine Learning Research
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ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
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ACL '02 Proceedings of the 40th 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
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Exploring correlation of dependency relation paths for answer extraction
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A word clustering approach for language model-based sentence retrieval in question answering systems
Proceedings of the 18th ACM conference on Information and knowledge management
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TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing
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NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
Answer extraction from passage graph for question answering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In this paper, we explore the syntactic relation patterns for open-domain factoid question answering. We propose a pattern extraction method to extract the various relations between the proper answers and different types of question words, including target words, head words, subject words and verbs, from syntactic trees. We further propose a QA-specific tree kernel to partially match the syntactic relation patterns. It makes the more tolerant matching between two patterns and helps to solve the data sparseness problem. Lastly, we incorporate the patterns into a Maximum Entropy Model to rank the answer candidates. The experiment on TREC questions shows that the syntactic relation patterns help to improve the performance by 6.91 MRR based on the common features.