Convolution Kernels for Opinion Holder Extraction
Michael Wiegand and Dietrich Klakow
In: NAACL 2010, 1 June - 6 June 2010, Los Angeles, CA, USA.
Opinion holder extraction is one of the important
subtasks in sentiment analysis. The effective
detection of an opinion holder depends
on the consideration of various cues on various
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 fragments
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 sequences
and trees. The definition of boundaries
in opinion holder extraction, however, is
less straightforward since there might be several
expressions beside the candidate opinion
holder to be eligible for being a boundary.