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- W2073187345 abstract "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 naive 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." @default.
- W2073187345 created "2016-06-24" @default.
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- W2073187345 date "2007-01-01" @default.
- W2073187345 modified "2023-09-24" @default.
- W2073187345 title "NUS-ML" @default.
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- W2073187345 doi "https://doi.org/10.3115/1621474.1621527" @default.
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