Syntactic and Semantic Structure for Opinion Expression Detection
Richard Johansson, Alessandro Moschitti · Institutional Research Information System (Università degli Studi di Trento) · 2010
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. 1