Paper: USF: Chunking for Aspect-term Identification & Polarity Classification

ACL ID S14-2140
Title USF: Chunking for Aspect-term Identification & Polarity Classification
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

This paper describes the systems submit- ted by the University of San Francisco (USF) to Semeval-2014 Task 4, Aspect Based Sentiment Analysis (ABSA), which provides labeled data in two domains, lap- tops and restaurants. For the constrained condition of both the aspect term extrac- tion and aspect term polarity tasks, we take a supervised machine learning approach using a combination of lexical, syntactic, and baseline sentiment features. Our ex- traction approach is inspired by a chunk- ing approach, based on its strong past re- sults on related tasks. Our system per- formed slightly below average compared to other submissions, possibly because we use a simpler classification model than prior work. Our polarity labeling ap- proach uses two baseline hand-built sen- timent classifiers as fe...