Paper: Convolution Kernels for Opinion Holder Extraction

ACL ID N10-1121
Title Convolution Kernels for Opinion Holder Extraction
Venue Human Language Technologies
Session Main Conference
Year 2010

Opinion holder extraction is one of the impor- tant subtasks in sentiment analysis. The ef- fective detection of an opinion holder depends on the consideration of various cues on vari- ous levels of representation, though they are hard to formulate explicitly as features. In this work, we propose to use convolution kernels for that task which identify meaningful frag- ments of sequences or trees by themselves. We not only investigate how different levels of information can be effectively combined in different kernels but also examine how the scope of these kernels should be chosen. In general relation extraction, the two candidate entities thought to be involved in a relation are commonly chosen to be the boundaries of se- quences and trees. The definition of bound- aries in opinion holder...