Word association norms, mutual information, and lexicography
Computational Linguistics
Class-based n-gram models of natural language
Computational Linguistics
Structural ambiguity and lexical relations
Computational Linguistics - Special issue on using large corpora: I
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Dealing with conjunctions in a machine translation environment
EACL '83 Proceedings of the first conference on European chapter of the Association for Computational Linguistics
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
The Journal of Machine Learning Research
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Efficient third-order dependency parsers
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Dynamic programming for linear-time incremental parsing
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Dual decomposition for parsing with non-projective head automata
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Using web-scale N-grams to improve base NP parsing performance
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Web-scale features for full-scale parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Using large monolingual and bilingual corpora to improve coordination disambiguation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Exploiting web-derived selectional preference to improve statistical dependency parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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Prepositions and conjunctions are two of the largest remaining bottlenecks in parsing. Across various existing parsers, these two categories have the lowest accuracies, and mistakes made have consequences for down-stream applications. Prepositions and conjunctions are often assumed to depend on lexical dependencies for correct resolution. As lexical statistics based on the training set only are sparse, unlabeled data can help ameliorate this sparsity problem. By including unlabeled data features into a factorization of the problem which matches the representation of prepositions and conjunctions, we achieve a new state-of-the-art for English dependencies with 93.55% correct attachments on the current standard. Furthermore, conjunctions are attached with an accuracy of 90.8%, and prepositions with an accuracy of 87.4%.