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Generalizing Dependency Features for Opinion Mining

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If you have a question about this talk, please contact Diarmuid Ó Séaghdha.

At this session of the NLIP Reading Group we’ll be discussing the following paper:

Mahesh Joshi and Carolyn Penstein-Rosé. 2009. Generalizing Dependency Features for Opinion Mining. In Proceedings of ACL -IJCNLP-09.

Abstract: We explore how features based on syntactic dependency relations can be utilized to improve performance on opinion mining. Using a transformation of dependency relation triples, we convert them into “composite back-off features” that generalize better than the regular lexicalized dependency relation features. Experiments comparing our approach with several other approaches that generalize dependency features or ngrams demonstrate the utility of composite back-off features.

As this is a shorter-than-usual paper, the presentation will also draw on the following paper as background:

Shotaro Matsumoto, Hiroya Takamura and Manabu Okumura. 2005. Sentiment Classification Using Word Sub-sequences and Dependency Sub-trees. In Proceedings of PAKDD -05.

This talk is part of the Natural Language Processing Reading Group series.

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