Syntactic and semantic structure for opinion expression detection

Authors:
Richard Johansson;Alessandro Moschitti
Affiliations:
University of Trento, Trento (TN), Italy;University of Trento, Trento (TN), Italy
Venue:
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Year:
2010

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Abstract

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 improving over conventional models based on sequence labeling with local features. These features allow us to model the way opinionated expressions interact in a sentence over arbitrary distances. While the relational features make the prediction task more computationally expensive, we show that it can be tackled effectively by using a reranker. We evaluate a number of machine learning approaches for the reranker, and the best model results in a 10-point absolute improvement in soft recall on the MPQA corpus, while decreasing precision only slightly.