NDLI: Sentiment Classification using Automatically Extracted Subgraph Features

Content Provider	Semantic Scholar
Author	Arora, Shilpa Mayfield, Elijah Rosé, Carolyn Penstein Nyberg, Eric H.
Copyright Year	2010
Abstract	In this work, we propose a novel representation of text based on patterns derived from linguistic annotation graphs. We use a subgraph mining algorithm to automatically derive features as frequent subgraphs from the annotation graph. This process generates a very large number of features, many of which are highly correlated. We propose a genetic programming based approach to feature construction which creates a fixed number of strong classification predictors from these subgraphs. We evaluate the benefit gained from evolved structured features, when used in addition to the bag-of-words features, for a sentiment classification task.
Starting Page	131
Ending Page	139
Page Count	9
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://anthology.aclweb.org/W/W10/W10-0216.pdf
Alternate Webpage(s)	http://www.aclweb.org/anthology/W10-0216
Alternate Webpage(s)	http://wing.comp.nus.edu.sg/~antho/W/W10/W10-0216.pdf
Alternate Webpage(s)	http://www.cs.cmu.edu/~shilpaa/Naacl-SentC-AGFeat-GP.pdf
Alternate Webpage(s)	http://www.aclweb.org/anthology/W/W10/W10-0216.pdf
Alternate Webpage(s)	http://aclweb.org/anthology/W10-0216
Alternate Webpage(s)	http://www.cs.cmu.edu/~emayfiel/AroraMayfieldRoseNybergNAACL2010.pdf
Alternate Webpage(s)	http://aclweb.org/anthology//W/W10/W10-0216.pdf
Alternate Webpage(s)	http://aclweb.org/anthology-new/W/W10/W10-0216.pdf
Journal	HLT-NAACL 2010
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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