The nature of statistical learning theory
The nature of statistical learning theory
The Journal of Machine Learning Research
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Employing topic models for pattern-based semantic class discovery
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
Evaluating word sense disambiguation tools for information retrieval task
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
KSU KDD: Word sense induction by clustering in topic space
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
RALI: Automatic weighting of text window distances
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Towards an optimal weighting of context words based on distance
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Topic models for meaning similarity in context
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A part-of-speech lexicographic encoding for an evolutionary word sense disambiguation approach
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
A supervised method for lexical annotation of schema labels based on wikipedia
ER'12 Proceedings of the 31st international conference on Conceptual Modeling
Creating a system for lexical substitutions from scratch using crowdsourcing
Language Resources and Evaluation
Latent word context model for information retrieval
Information Retrieval
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We participated in SemEval-1 English coarse-grained all-words task (task 7), English fine-grained all-words task (task 17, subtask 3) and English coarse-grained lexical sample task (task 17, subtask 1). The same method with different labeled data is used for the tasks; SemCor is the labeled corpus used to train our system for the all-words tasks while the labeled corpus that is provided is used for the lexical sample task. The knowledge sources include part-of-speech of neighboring words, single words in the surrounding context, local collocations, and syntactic patterns. In addition, we constructed a topic feature, targeted to capture the global context information, using the latent dirichlet allocation (LDA) algorithm with unlabeled corpus. A modified naïve Bayes classifier is constructed to incorporate all the features. We achieved 81.6%, 57.6%, 88.7% for coarse-grained all-words task, fine-grained all-words task and coarse-grained lexical sample task respectively.