Paper: Joint Inference for Fine-grained Opinion Extraction

ACL ID P13-1161
Title Joint Inference for Fine-grained Opinion Extraction
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

This paper addresses the task of fine- grained opinion extraction ? the identi- fication of opinion-related entities: the opinion expressions, the opinion hold- ers, and the targets of the opinions, and the relations between opinion expressions and their targets and holders. Most ex- isting approaches tackle the extraction of opinion entities and opinion relations in a pipelined manner, where the inter- dependencies among different extraction stages are not captured. We propose a joint inference model that leverages knowledge from predictors that optimize subtasks of opinion extraction, and seeks a glob- ally optimal solution. Experimental re- sults demonstrate that our joint inference approach significantly outperforms tradi- tional pipeline methods and baselines that tackle subtasks in i...