Making large-scale support vector machine learning practical
Advances in kernel methods
Scaling question answering to the Web
Proceedings of the 10th international conference on World Wide Web
Kernel methods for relation extraction
The Journal of Machine Learning Research
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
The effect of document retrieval quality on factoid question answering performance
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Ranking definitions with supervised learning methods
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Generic soft pattern models for definitional question answering
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning question classifiers: the role of semantic information
Natural Language Engineering
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
Hierarchical semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Using syntactic information for improving why-question answering
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
What is not in the bag of words for why-qa?
Computational Linguistics
Proceedings of the 5th International Workshop on Web APIs and Service Mashups
A support vector machine-based context-ranking model for question answering
Information Sciences: an International Journal
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In this paper, we study novel structures to represent information in three vital tasks in question answering: question classification, answer classification and answer reranking. We define a new tree structure called PAS to represent predicate-argument relations, as well as a new kernel function to exploit its representative power. Our experiments with Support Vector Machines and several tree kernel functions suggest that syntactic information helps specific task as question classification, whereas, when data sparseness is higher as in answer classification, studying coarse semantic information like PAS is a promising research area.