The nature of statistical learning theory
The nature of statistical learning theory
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Three generative, lexicalised models for statistical parsing
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
Error-driven pruning of Treebank grammars for base noun phrase identification
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Text chunking using regularized Winnow
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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
Use of support vector learning for chunk identification
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Introduction to the CoNLL-2002 shared task: language-independent named entity recognition
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Probabilistic CFG with latent annotations
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd 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
Noun phrase chunking in Hebrew: influence of lexical and morphological features
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Discriminative learning and spanning tree algorithms for dependency parsing
Discriminative learning and spanning tree algorithms for dependency parsing
A unified architecture for natural language processing: deep neural networks with multitask learning
Proceedings of the 25th international conference on Machine learning
Minimally lexicalized dependency parsing
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Design challenges and misconceptions in named entity recognition
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Towards open-domain Semantic Role Labeling
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
GEMS '10 Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics
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We use the technique of SVM anchoring to demonstrate that lexical features extracted from a training corpus are not necessary to obtain state of the art results on tasks such as Named Entity Recognition and Chunking. While standard models require as many as 100K distinct features, we derive models with as little as 1K features that perform as well or better on different domains. These robust reduced models indicate that the way rare lexical features contribute to classification in NLP is not fully understood. Contrastive error analysis (with and without lexical features) indicates that lexical features do contribute to resolving some semantic and complex syntactic ambiguities -- but we find this contribution does not generalize outside the training corpus. As a general strategy, we believe lexical features should not be directly derived from a training corpus but instead, carefully inferred and selected from other sources.