C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A maximum entropy approach to natural language processing
Computational Linguistics
Feature selection in SVM text categorization
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Extracting important sentences with support vector machines
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
An analysis of a high-performance japanese question answering system
ACM Transactions on Asian Language Information Processing (TALIP)
Question classification using HDAG kernel
MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
Question answering as question-biased term extraction: a new approach toward multilingual QA
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Mining redundancy in candidate-bearing snippets to improve web question answering
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Studying feature generation from various data representations for answer extraction
FeatureEng '05 Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing
Linguistic kernels for answer re-ranking in question answering systems
Information Processing and Management: an International Journal
Selecting Answers to Questions from Web Documents by a Robust Validation Process
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Using syntactic and semantic structural kernels for classifying definition questions in Jeopardy!
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Knowledge element extraction for knowledge-based learning resources organization
ICWL'07 Proceedings of the 6th international conference on Advances in web based learning
Structural relationships for large-scale learning of answer re-ranking
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Learning to rank for robust question answering
Proceedings of the 21st ACM international conference on Information and knowledge management
A support vector machine-based context-ranking model for question answering
Information Sciences: an International Journal
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This paper presents an answer selection method based on Support Vector Machines (SVM) for Open-Domain Question Answering (QA). Selecting and ranking plausible answers from a large number of candidates in documents is one of the most critical parts of QA systems. It is extremely difficult to find good evaluation functions or rules for the answer selection. To overcome this issue, we apply SVM to answer selection. We evaluate the performance measured by mean reciprocal rank (MRR) and the correct ratio of answer ranked first. The results show that the proposed SVM-based method offers a statistically significant increase in performance compared to other machine learning methods such as decision tree learning (C4.5) boosting with decision tree learning (C5.0), and the maximum entropy method.