Paper: Identifying High-Impact Sub-Structures for Convolution Kernels in Document-level Sentiment Classification

ACL ID P12-2066
Title Identifying High-Impact Sub-Structures for Convolution Kernels in Document-level Sentiment Classification
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2012
Authors

Convolution kernels support the modeling of complex syntactic information in machine- learning tasks. However, such models are highly sensitive to the type and size of syntac- tic structure used. It is therefore an importan- t challenge to automatically identify high im- pact sub-structures relevant to a given task. In this paper we present a systematic study inves- tigating (combinations of) sequence and con- volution kernels using different types of sub- structures in document-level sentiment classi- fication. We show that minimal sub-structures extracted from constituency and dependency trees guided by a polarity lexicon show 1.45 point absolute improvement in accuracy over a bag-of-words classifier on a widely used sen- timent corpus.