Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
Ranking definitions with supervised learning methods
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Probabilistic model for definitional question answering
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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
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As an important form of complex questions, definition question attracts much attention from QA researchers. For many of the definition question answering systems, it is a core step to rank the candidate answer sentences, so that the top-k in the ranked list can be extracted. We integrate these evidences as features into a whole framework, and propose a novel method to learning weights of these features to rank the candidate answer sentences.