Paper: Subjectivity Recognition on Word Senses via Semi-supervised Mincuts

ACL ID N09-1001
Title Subjectivity Recognition on Word Senses via Semi-supervised Mincuts
Venue Human Language Technologies
Session Main Conference
Year 2009
Authors

We supplement WordNet entries with infor- mation on the subjectivity of its word senses. Supervised classifiers that operate on word sense definitions in the same way that text classifiers operate on web or newspaper texts need large amounts of training data. The re- sulting data sparseness problem is aggravated by the fact that dictionary definitions are very short. We propose a semi-supervised mini- mum cut framework that makes use of both WordNet definitions and its relation structure. The experimental results show that it outper- formssupervisedminimumcutaswellasstan- dard supervised, non-graph classification, re- ducing the error rate by 40%. In addition, the semi-supervised approach achieves the same results as the supervised framework with less than 20% of the training data.