On building a more efficient grammar by exploiting types
Natural Language Engineering
Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Hybrid learning of dependency structures from heterogeneous linguistic resources
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Recognizing textual entailment using a subsequence kernel method
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
SemEval-2010 task 12: Parser evaluation using textual entailments
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
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This paper describes our participation in the the SemEval-2010 Task #12, Parser Evaluation using Textual Entailment. Our system incorporated two dependency parsers, one semantic role labeler, and a deep parser based on hand-crafted grammars. The shortest path algorithm is applied on the graph representation of the parser outputs. Then, different types of features are extracted and the entailment recognition is casted into a machine-learning-based classification task. The best setting of the system achieves 66.78% of accuracy, which ranks the 3rd place.