Identifying high-impact sub-structures for convolution kernels in document-level sentiment classification
Tu, Zhaopeng, He, Yifan, Foster, JenniferORCID: 0000-0002-7789-4853, van Genabith, JosefORCID: 0000-0003-1322-7944, Liu, Qun and Shouxun, Lin
(2012)
Identifying high-impact sub-structures for convolution kernels in document-level sentiment classification.
In: Annual Meeting of the Association for Computational Linguistics (ACL 2012), 9-11 Jul 2012, Jelu, Korea.
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 syntactic structure used. It is therefore an important challenge to automatically identify high impact sub-structures relevant to a given task. In this paper we present a systematic study investigating (combinations of) sequence and convolution kernels using different types of substructures in document-level sentiment classification. 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 sentiment corpus.
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
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Association for Computational Linguistics.