Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
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NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Inductive Dependency Parsing (Text, Speech and Language Technology)
Inductive Dependency Parsing (Text, Speech and Language Technology)
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ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
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DeepLP '07 Proceedings of the Workshop on Deep Linguistic Processing
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IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
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EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
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ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
SPMRL '10 Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages
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HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Parsing the penn chinese treebank with semantic knowledge
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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ACM Transactions on Speech and Language Processing (TSLP) - Special issue on multiword expressions: From theory to practice and use, part 2
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This paper presents the introduction of WordNet semantic classes in a dependency parser, obtaining improvements on the full Penn Treebank for the first time. We tried different combinations of some basic semantic classes and word sense disambiguation algorithms. Our experiments show that selecting the adequate combination of semantic features on development data is key for success. Given the basic nature of the semantic classes and word sense disambiguation algorithms used, we think there is ample room for future improvements.