CGM: A biomedical text categorization approach using concept graph mining

Document Type

Conference Proceeding

Publication Date

12-1-2009

Abstract

Text Categorization is used to organize and manage biomedical text databases that are growing at an exponential rate. Feature representations for documents are a crucial factor for the performance of text categorization. Most of the successful existing techniques use a vector representation based on key entities extracted from the text. In this paper we investigate a new direction where we represent a document as a graph. In this representation we identify high level concepts and build a rich graph structure that contains additional concepts and relationships. We then use graph kernel techniques to perform text categorization. The results show a significant improvement in accuracy when compared to categorization based on only the extracted concepts. ©2009 IEEE.

Identifier

72849119302 (Scopus)

ISBN

[9781424451210]

Publication Title

Proceedings 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops Bibmw 2009

External Full Text Location

https://doi.org/10.1109/BIBMW.2009.5332134

First Page

38

Last Page

43

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