A Maximum-Entropy-Inspired Parser
A Maximum-Entropy-Inspired Parser
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Intricacies of Collins' Parsing Model
Computational Linguistics
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Machine translation using probabilistic synchronous dependency insertion grammars
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank
Computational Linguistics
The Stanford typed dependencies representation
CrossParser '08 Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
Intraclausal Coordination and Clause Detection as a Preprocessing Step to Dependency Parsing
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
A dependency-based method for evaluating broad-coverage parsers
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A generative re-ranking model for dependency parsing
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
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We present a probabilistic model extension to the Tesnière Dependency Structure (TDS) framework formulated in (Sangati and Mazza, 2009). This representation incorporates aspects from both constituency and dependency theory. In addition, it makes use of junction structures to handle coordination constructions. We test our model on parsing the English Penn WSJ treebank using a re-ranking framework. This technique allows us to efficiently test our model without needing a specialized parser, and to use the standard evaluation metric on the original Phrase Structure version of the treebank. We obtain encouraging results: we achieve a small improvement over state-of-the-art results when re-ranking a small number of candidate structures, on all the evaluation metrics except for chunking.