TY - GEN
T1 - Incorporating Background Knowledge into Text Classification
AU - Boghrati, Reihane
AU - Garten, Justin
AU - Litvinova, Aleksandra
AU - Dehghani, Morteza
N1 - Publisher Copyright:
© Cognitive Science Society, CogSci 2015.All rights reserved.
PY - 2015
Y1 - 2015
N2 - It has been shown that prior knowledge and information are organized according to categories, and that also background knowledge plays an important role in classification. The purpose of this study is first, to investigate the relationship between background knowledge and text classification, and second, to incorporate this relationship in a computational model. Our behavioral results demonstrate that participants with access to background knowledge (experts), overall performed significantly better than those without access to this knowledge (novices). More importantly, we show that experts rely more on relational features than surface features, an aspect that bag-of-words methods fail to capture. We then propose a computational model for text classification which incorporates background knowledge. This model is built upon vector-based representation methods and achieves significantly more accurate results over other models that were tested.
AB - It has been shown that prior knowledge and information are organized according to categories, and that also background knowledge plays an important role in classification. The purpose of this study is first, to investigate the relationship between background knowledge and text classification, and second, to incorporate this relationship in a computational model. Our behavioral results demonstrate that participants with access to background knowledge (experts), overall performed significantly better than those without access to this knowledge (novices). More importantly, we show that experts rely more on relational features than surface features, an aspect that bag-of-words methods fail to capture. We then propose a computational model for text classification which incorporates background knowledge. This model is built upon vector-based representation methods and achieves significantly more accurate results over other models that were tested.
KW - background knowledge
KW - distributed representation
KW - similarity
KW - text classification
UR - http://www.scopus.com/inward/record.url?scp=85117985910&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85117985910
T3 - Proceedings of the 37th Annual Meeting of the Cognitive Science Society, CogSci 2015
SP - 244
EP - 249
BT - Proceedings of the 37th Annual Meeting of the Cognitive Science Society, CogSci 2015
A2 - Noelle, David C.
A2 - Dale, Rick
A2 - Warlaumont, Anne
A2 - Yoshimi, Jeff
A2 - Matlock, Teenie
A2 - Jennings, Carolyn D.
A2 - Maglio, Paul P.
PB - The Cognitive Science Society
T2 - 37th Annual Meeting of the Cognitive Science Society: Mind, Technology, and Society, CogSci 2015
Y2 - 23 July 2015 through 25 July 2015
ER -