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	title        = {Mining Fine-grained Opinion Expressions with Shallow Parsing},
	abstract     = {Opinion analysis deals with public opinions and trends, but subjective language
is highly ambiguous. In this paper, we
follow a simple data-driven technique to
learn fine-grained opinions. We select
an intersection set of Wall Street Journal documents that is included both in the
Penn Discourse Tree Bank (PDTB) and in
the Multi-Perspective Question Answering (MPQA) corpus. This is done in order to explore the usefulness of discourse-level structure to facilitate the extraction
of fine-grained opinion expressions. Here
we perform shallow parsing of MPQA expressions with connective based discourse
structure, and then also with Named Entities (NE) and some syntax features using
conditional random fields; the latter feature set is basically a collection of NEs and
a bundle of features that is proved to be
useful in a shallow discourse parsing task.
We found that both of the feature-sets are
useful to improve our baseline at different
levels of this fine-grained opinion expression mining task.},
	booktitle    = {Proceedings of the International Conference Recent Advances in Natural Language Processing},
	author       = {Ghosh, Sucheta and Tonelli, Sara and Johansson, Richard},
	year         = {2013},
	pages        = {302--310},