Class-based n-gram models of natural language
Computational Linguistics
The nature of statistical learning theory
The nature of statistical learning theory
A maximum entropy approach to natural language processing
Computational Linguistics
Probabilistic question answering on the web
Proceedings of the 11th international conference on World Wide Web
Computing Optimal Hypotheses Efficiently for Boosting
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Question classification with support vector machines and error correcting codes
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Question classification using head words and their hypernyms
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Subtree mining for question classification problem
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A semantic approach for question classification using WordNet and Wikipedia
Pattern Recognition Letters
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Question classification has been recognized as a very important step for many natural language applications (i.e question answering). Subtree mining has been indicated that [10] it is helpful for question classification problem. The authors empirically showed that subtree features obtained by subtree mining, were able to improve the performance of Question Classification for boosting and maximum entropy models. In this paper, our first goal is to investigate that whether or not subtree mining features are useful for structured support vector machines. Secondly, to make the proposed models more robust, we incorporate subtree features with word-cluster models gained from a large collection of text documents. Experimental results show that the uses of word-cluster models with subtree mining can significantly improve the performance of the proposed question classification models.