Paper: Context-aware Learning for Sentence-level Sentiment Analysis with Posterior Regularization

ACL ID P14-1031
Title Context-aware Learning for Sentence-level Sentiment Analysis with Posterior Regularization
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

This paper proposes a novel context-aware method for analyzing sentiment at the level of individual sentences. Most ex- isting machine learning approaches suf- fer from limitations in the modeling of complex linguistic structures across sen- tences and often fail to capture non- local contextual cues that are important for sentiment interpretation. In contrast, our approach allows structured modeling of sentiment while taking into account both local and global contextual infor- mation. Specifically, we encode intu- itive lexical and discourse knowledge as expressive constraints and integrate them into the learning of conditional random field models via posterior regularization. The context-aware constraints provide ad- ditional power to the CRF model and can guide semi-supervised learning ...