Paper: A Vector Space Model for Subjectivity Classification in Urdu aided by Co-Training

ACL ID C10-2099
Title A Vector Space Model for Subjectivity Classification in Urdu aided by Co-Training
Venue International Conference on Computational Linguistics
Session Poster Session
Year 2010
Authors

The goal of this work is to produce a classifier that can distinguish subjective sentences from objective sentences for the Urdu language. The amount of la- beled data required for training automatic classifiers can be highly imbalanced es- pecially in the multilingual paradigm as generating annotations is an expensive task. In this work, we propose a co- training approach for subjectivity analy- sis in the Urdu language that augments the positive set (subjective set) and gene- rates a negative set (objective set) devoid of all samples close to the positive ones. Using the data set thus generated for training, we conduct experiments based on SVM and VSM algorithms, and show that our modified VSM based approach works remarkably well as a sentence lev- el subjectivity classifie...