Genetic Algorithms and Machine Learning
Machine Learning
Head/modifier pairs for everyone
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
ACM SIGIR Forum
A question/answer typology with surface text patterns
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Passage retrieval for question answering using sliding windows
IRQA '08 Coling 2008: Proceedings of the 2nd workshop on Information Retrieval for Question Answering
Advanced structural representations for question classification and answer re-ranking
ECIR'07 Proceedings of the 29th European conference on IR research
Evaluating paragraph retrieval for why-QA
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Improving passage retrieval in question answering using NLP
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Passage retrieval for question answering using sliding windows
IRQA '08 Coling 2008: Proceedings of the 2nd workshop on Information Retrieval for Question Answering
Why text segment classification based on part of speech feature selection
DS'10 Proceedings of the 13th international conference on Discovery science
Learning to rank for why-question answering
Information Retrieval
A survey on question answering technology from an information retrieval perspective
Information Sciences: an International Journal
Towards domain independent why text segment classification based on bag of function words
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Semantic models for answer re-ranking in question answering
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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In this paper, we extend an existing paragraph retrieval approach to why-question answering. The starting-point is a system that retrieves a relevant answer for 73% of the test questions. However, in 41% of these cases, the highest ranked relevant answer is not ranked in the top-10. We aim to improve the ranking by adding a re-ranking module. For re-ranking we consider 31 features pertaining to the syntactic structure of the question and the candidate answer. We find a significant improvement over the baseline for both success@10 and MRR@150. The most important features for re-ranking are the baseline score, the presence of cue words, the question's main verb, and the relation between question focus and document title.