Paper: Interactive Annotation Learning with Indirect Feature Voting

ACL ID N09-3010
Title Interactive Annotation Learning with Indirect Feature Voting
Venue HLT-NAACL Companion Volume: Student Research Workshop and Doctoral Consortium
Session
Year 2009
Authors

We demonstrate that a supervised annotation learning approach using structured features derived from tokens and prior annotations per- forms better than a bag of words approach. We present a general graph representation for automatically deriving these features from la- beled data. Automatic feature selection based on class association scores requires a large amount of labeled data and direct voting can be difficult and error-prone for structured fea- tures, even for language specialists. We show that highlighted rationales from the user can be used for indirect feature voting and same performance can be achieved with less labeled data.We present our results on two annotation learning tasks for opinion mining from prod- uct and movie reviews.