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 allwords 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 allwords task, fine-grained all-words task and coarse-grained lexical sample task respectively.
In REF , LDA is utilized to capture the global context information as the topic features for better performing the WSD task.
NUS-ML:Improving Word Sense Disambiguation Using Topic Features
Fourth International Workshop on Semantic Evaluations (SemEval-2007)
Computer Science