The nature of statistical learning theory
The nature of statistical learning theory
Foundations of statistical natural language processing
Foundations of statistical natural language processing
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
The Journal of Machine Learning Research
Semantic passage segmentation based on sentence topics for question answering
Information Sciences: an International Journal
Robust and efficient multiclass SVM models for phrase pattern recognition
Pattern Recognition
The third PASCAL recognizing textual entailment challenge
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Recognizing textual entailment using sentence similarity based on dependency tree skeletons
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Learning textual entailment using SVMs and string similarity measures
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Machine learning based semantic inference: experiments and observations at RTE-3
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Paraphrase recognition using machine learning to combine similarity measures
ACLstudent '09 Proceedings of the ACL-IJCNLP 2009 Student Research Workshop
Expert Systems with Applications: An International Journal
A survey of paraphrasing and textual entailment methods
Journal of Artificial Intelligence Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A novel approach to update summarization using evolutionary manifold-ranking and spectral clustering
Expert Systems with Applications: An International Journal
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Recognizing textual entailment is to infer that a given text span follows from the meaning of a given hypothesis. To have better recognition capability, it is necessary to employ deep text processing units such as syntactic parsers and semantic taggers. However, these resources are not usually available in other non-English languages. In this paper, we present a light-weight Chinese textual entailment recognition system using part-of-speech information only. We designed two different feature models from training data and employed the well-known kernel method to learn to predict testing data. One feature set abstracts the generic statistics between the text pairs, while the other set directly models lexical features based on the traditional bag-of-words model. The ability of the proposed feature models not only brings additional statistical information from their datasets but also helps to enhance the prediction capability. To validate this, we conducted the experiments on the novel benchmark corpus - NTCIR-RITE-2011. The empirical results demonstrate that our method achieves the best results in comparison to the other competitors. In terms of accuracy, our method achieves 54.77% for the NTCIR RITE MC task.