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
Automatic rule induction for unknown-word guessing
Computational Linguistics
A non-projective dependency parser
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Comparing a linguistic and a stochastic tagger
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Using grammatical relations to compare parsers
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Robust, applied morphological generation
INLG '00 Proceedings of the first international conference on Natural language generation - Volume 14
A dependency-based method for evaluating broad-coverage parsers
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Towards a semantic classification of Spanish verbs based on subcategorisation information
ACLstudent '04 Proceedings of the ACL 2004 workshop on Student research
A robust and hybrid deep-linguistic theory applied to large-scale parsing
ROMAND '04 Proceedings of the 3rd Workshop on RObust Methods in Analysis of Natural Language Data
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Large-scale parsing is still a complex and time-consuming process, often so much that it is infeasible in real-world applications. The parsing system described here addresses this problem by combining finite-state approaches, statistical parsing techniques and engineering knowledge, thus keeping parsing complexity as low as possible at the cost of a slight decrease in performance. The parser is robust and fast and at the same time based on strong linguistic foundations.