Exploiting redundancy in question answering
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
On the MSE robustness of batching estimators
Proceedings of the 33nd conference on Winter simulation
Unsupervised learning of soft patterns for generating definitions from online news
Proceedings of the 13th international conference on World Wide Web
The automated acquisition of topic signatures for text summarization
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Is it the right answer?: exploiting web redundancy for Answer Validation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Reranking answers for definitional QA using language modeling
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Automatically evaluating answers to definition questions
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Interesting nuggets and their impact on definitional question answering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Lightweight web-based fact repositories for textual question answering
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
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In order to build accurate target profiles, most definition question answering (QA) systems primarily involve utilizing various external resources, such as WordNet, Wikipedia, Biograpy.com, etc. However, these external resources are not always available or helpful when answering definition questions. In contrast, this paper proposes an unsupervised classification model, called the U-Model, which can liberate definitional QA systems from heavily depending on a variety of external resources via applying sentence expansion ($SE$) and SVM classifier. Experimental results from testing on English TREC test sets reveal that the proposed U-Model can not only significantly outperform baseline system but also require no specific external resources.