We describe the implementation of reranking models for fine-grained opinion analysis -marking up opinion expressions and extracting opinion holders. The reranking approach makes it possible to model complex relations between multiple opinions in a sentence, allowing us to represent how opinions interact through the syntactic and semantic structure. We carried out evaluations on the MPQA corpus, and the experiments showed significant improvements over a conventional system that only uses local information: for both tasks, our system saw recall boosts of over 10 points.
REF extract opinion expressions and holders by applying reranking on top of sequence labeling methods.
Reranking Models in Fine-grained Opinion Analysis
COLING
Computer Science