A maximum entropy approach to natural language processing
Computational Linguistics
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
Assigning function tags to parsed text
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
COLING-GEE '02 Proceedings of the 2002 workshop on Grammar engineering and evaluation - Volume 15
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Learning accurate, compact, and interpretable tree annotation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Semantic role labeling via integer linear programming inference
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Hidden-variable models for discriminative reranking
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Using machine-learning to assign function labels to parser output for Spanish
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Wide-coverage deep statistical parsing using automatic dependency structure annotation
Computational Linguistics
The importance of syntactic parsing and inference in semantic role labeling
Computational Linguistics
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Relational-realizational parsing
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Filling statistics with linguistics: property design for the disambiguation of German LFG parses
DeepLP '07 Proceedings of the Workshop on Deep Linguistic Processing
Global inference for sentence compression an integer linear programming approach
Journal of Artificial Intelligence Research
Concise integer linear programming formulations for dependency parsing
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Informed ways of improving data-driven dependency parsing for German
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Detecting dependency parse errors with minimal resources
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
Data point selection for self-training
SPMRL '11 Proceedings of the Second Workshop on Statistical Parsing of Morphologically Rich Languages
Morphological and syntactic case in statistical dependency parsing
Computational Linguistics
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For languages with (semi-) free word order (such as German), labelling grammatical functions on top of phrase-structural constituent analyses is crucial for making them interpretable. Unfortunately, most statistical classifiers consider only local information for function labelling and fail to capture important restrictions on the distribution of core argument functions such as subject, object etc., namely that there is at most one subject (etc.) per clause. We augment a statistical classifier with an integer linear program imposing hard linguistic constraints on the solution space output by the classifier, capturing global distributional restrictions. We show that this improves labelling quality, in particular for argument grammatical functions, in an intrinsic evaluation, and, importantly, grammar coverage for treebank-based (Lexical-Functional) grammar acquisition and parsing, in an extrinsic evaluation.