On the MSE robustness of batching estimators
Proceedings of the 33nd conference on Winter simulation
Answer models for question answering passage retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Chinese named entity recognition based on multiple features
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A novel pattern learning method for open domain question answering
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
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This paper presents two novel clustering approaches and their application to open-domain question answering. The One-Sentence-Multi-Topicclustering approach is first presented, which clusters sentences to improve the language model for retrieving sentences. Second, regarding each cluster in the results for One-Sentence-Multi-Topicclustering as aligned sentences, we present a pattern-similarity-based clustering approach that automatically learns syntactic answer patterns to answer selection through verticaland horizontal clustering. Our experiments on Chinese question answering demonstrates that One-Sentence-Multi-Topicclustering is much better than K-Means and is comparable to PLSI when used in sentence clustering of question answering. Similarly, the pattern-similarity-based clustering also proved to be efficient in learning syntactic answer patterns, the absolute improvement in syntactic pattern-based answer extraction over retrieval-based answer extraction is about 9%.