Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Statistical models for unsupervised prepositional phrase attachment
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
A connectionist approach to prepositional phrase attachment for real world texts
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Structural ambiguity and lexical relations
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
A rule-based approach to prepositional phrase attachment disambiguation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Combining unsupervised and supervised methods for PP attachment disambiguation
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
An unsupervised approach to prepositional phrase attachment using contextually similar words
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
A maximum entropy model for prepositional phrase attachment
HLT '94 Proceedings of the workshop on Human Language Technology
What to do when lexicalization fails: parsing German with suffix analysis and smoothing
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Hybrid parsing: using probabilistic models as predictors for a symbolic parser
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Annotation strategies for probabilistic parsing in German
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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
A statistical constraint dependency grammar (CDG) parser
IncrementParsing '04 Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together
Parsing unrestricted german text with defeasible constraints
CSLP'04 Proceedings of the First international conference on Constraint Solving and Language Processing
Hybrid parsing: using probabilistic models as predictors for a symbolic parser
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Prepositions in applications: A survey and introduction to the special issue
Computational Linguistics
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To study PP attachment disambiguation as a benchmark for empirical methods in natural language processing it has often been reduced to a binary decision problem (between verb or noun attachment) in a particular syntactic configuration. A parser, however, must solve the more general task of deciding between more than two alternatives in many different contexts. We combine the attachment predictions made by a simple model of lexical attraction with a full-fledged parser of German to determine the actual benefit of the subtask to parsing. We show that the combination of data-driven and rule-based components can reduce the number of all parsing errors by 14% and raise the attachment accuracy for dependency parsing of German to an unprecedented 92%.