Sentiment Classification Using Automatically Extracted Subgraph Features
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Arora:2010:NAACL,
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author = "Shilpa Arora and Elijah Mayfield and
Carolyn Penstein-Rose and Eric Nyberg",
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title = "Sentiment Classification Using Automatically Extracted
Subgraph Features",
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booktitle = "Proceedings of the NAACL HLT 2010 Workshop on
Computational Approaches to Analysis and Generation of
Emotion in Text",
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series = "CAAGET '10",
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year = "2010",
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address = "Los Angeles, California",
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pages = "131--139",
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month = jun,
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keywords = "genetic algorithms, genetic programming, GP",
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URL = "http://dl.acm.org/citation.cfm?id=1860631.1860647",
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acmid = "1860647",
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oai = "oai:CiteSeerX.psu:10.1.1.207.7440",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.207.7440",
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URL = "http://www.cs.cmu.edu/%7Eemayfiel/AroraMayfieldRoseNybergNAACL2010.pdf",
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publisher = "Association for Computational Linguistics",
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publisher_address = "Stroudsburg, PA, USA",
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size = "9 pages",
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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.",
- }
Genetic Programming entries for
Shilpa Arora
Elijah Mayfield
Carolyn Penstein Rose
Eric Nyberg
Citations