Paper: Syntactic and Semantic Structure for Opinion Expression Detection

ACL ID W10-2910
Title Syntactic and Semantic Structure for Opinion Expression Detection
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2010
Authors

We demonstrate that relational features derived from dependency-syntactic and semantic role structures are useful for the task of detecting opinionated expressions in natural-language text, significantly im- proving over conventional models based on sequence labeling with local features. These features allow us to model the way opinionated expressions interact in a sen- tence over arbitrary distances. While the relational features make the pre- diction task more computationally expen- sive, we show that it can be tackled effec- tively by using a reranker. We evaluate a number of machine learning approaches for the reranker, and the best model re- sults in a 10-point absolute improvement in soft recall on the MPQA corpus, while decreasing precision only slightly.