PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Convolution Kernels for Subjectivity Detection
Michael Wiegand and Dietrich Klakow
In: NODALIDA 2011, 11 May - 13 May 2011, Riga, Latvia.


In this paper, we explore different linguistic structures encoded as convolution kernels for the detection of subjective expressions. The advantage of convolution kernels is that complex structures can be directly provided to a classifier without deriving explicit features. The feature design for the detection of subjective expressions is fairly difficult and there currently exists no commonly accepted feature set. We consider various structures, such as constituency parse structures, dependency parse structures, and predicate-argument structures. In order to generalize from lexical information, we additionally augment these structures with clustering information and the task-specific knowledge of subjective words. The convolution kernels will be compared with a standard vector kernel.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:8852
Deposited By:Diana Schreyer
Deposited On:21 February 2012