Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth 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
Large-Scale learning of relation-extraction rules with distant supervision from the web
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
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We compare several state of the art dependency parsers with our own parser based on a linear classification technique. Our primary goal is therefore to use syntactic information only, in order to keep the comparison of the parsers as fair as possible. We demonstrate, that despite the inferior result using the standard evaluation metrics for parsers like UAS or LAS on standard test data, our system achieves comparable results when used in an application, such as the SemEval-2 #12 evaluation exercise PETE. Our submission achieved the 4th position out of 19 participating systems. However, since it only uses a linear classifier it works 17--20 times faster than other state of the parsers, as for instance MaltParser or Stanford Parser.