Paper: Detection of Product Comparisons - How Far Does an Out-of-the-Box Semantic Role Labeling System Take You?

ACL ID D13-1194
Title Detection of Product Comparisons - How Far Does an Out-of-the-Box Semantic Role Labeling System Take You?
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

This short paper presents a pilot study in- vestigating the training of a standard Seman- tic Role Labeling (SRL) system on product reviews for the new task of detecting com- parisons. An (opinionated) comparison con- sists of a comparative ?predicate? and up to three ?arguments?: the entity evaluated posi- tively, the entity evaluated negatively, and the aspect under which the comparison is made. In user-generated product reviews, the ?predi- cate? and ?arguments? are expressed in highly heterogeneous ways; but since the elements are textually annotated in existing datasets, SRL is technically applicable. We address the interesting question how well training an out- of-the-box SRL model works for English data. We observe that even without any feature en- gineering or other major adaptions...