On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
NUS-ML: improving word sense disambiguation using topic features
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
A Reexamination of MRD-Based Word Sense Disambiguation
ACM Transactions on Asian Language Information Processing (TALIP)
SemEval-2010 task: Japanese WSD
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Word Sense Disambiguation by Combining Labeled Data Expansion and Semi-Supervised Learning Method
ACM Transactions on Asian Language Information Processing (TALIP)
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We participated in the SemEval-2010 Japanese Word Sense Disambiguation (WSD) task (Task 16) and focused on the following: (1) investigating domain differences, (2) incorporating topic features, and (3) predicting new unknown senses. We experimented with Support Vector Machines (SVM) and Maximum Entropy (MEM) classifiers. We achieved 80.1% accuracy in our experiments.