Paper: Hierarchical Sequential Learning for Extracting Opinions and Their Attributes

ACL ID P10-2050
Title Hierarchical Sequential Learning for Extracting Opinions and Their Attributes
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2010
Authors

Automatic opinion recognition involves a number of related tasks, such as identi- fying the boundaries of opinion expres- sion, determining their polarity, and de- termining their intensity. Although much progress has been made in this area, ex- isting research typically treats each of the above tasks in isolation. In this paper, we apply a hierarchical parameter shar- ing technique using Conditional Random Fields for fine-grained opinion analysis, jointly detecting the boundaries of opinion expressions as well as determining two of their key attributes — polarity and inten- sity. Our experimental results show that our proposed approach improves the per- formance over a baseline that does not exploit hierarchical structure among the classes. In addition, we find that the joint approach o...