We explore how features based on syntactic dependency relations can be utilized to improve performance on opinion mining. Using a transformation of dependency relation triples, we convert them into "composite back-off features" that generalize better than the regular lexicalized dependency relation features. Experiments comparing our approach with several other approaches that generalize dependency features or ngrams demonstrate the utility of composite back-off features.
Joshi et al. REF used a transformation of dependency relation triples as additional features to unigrams and yielded a better performance than using purely lexicalized dependency relations.
Generalizing Dependency Features for Opinion Mining
ACL-IJCNLP: Short Papers
Computer Science