Paper: Learning Subjective Nouns Using Extraction Pattern Bootstrapping

ACL ID W03-0404
Title Learning Subjective Nouns Using Extraction Pattern Bootstrapping
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2003
Authors

We explore the idea of creating a subjectiv- ity classifier that uses lists of subjective nouns learned by bootstrapping algorithms. The goal of our research is to develop a system that can distinguish subjective sentences from ob- jective sentences. First, we use two bootstrap- ping algorithms that exploit extraction patterns to learn sets of subjective nouns. Then we train a Naive Bayes classifier using the subjec- tive nouns, discourse features, and subjectivity clues identified in prior research. The boot- strapping algorithms learned over 1000 subjec- tive nouns, and the subjectivity classifier per- formed well, achieving 77% recall with 81% precision.