CGM: A biomedical text categorization approach using concept graph mining

Said Bleik; Min Song; Aaron Smalter; Jun Huan; Gerald Lushington

doi:10.1109/BIBMW.2009.5332134

CGM: A biomedical text categorization approach using concept graph mining

Said Bleik, Min Song, Aaron Smalter, Jun Huan, Gerald Lushington

Department of Library and Information Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009
Pages	38-43
Number of pages	6
DOIs	https://doi.org/10.1109/BIBMW.2009.5332134
Publication status	Published - 2009
Event	2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009 - Washington, DC, United States Duration: 2009 Nov 1 → 2009 Nov 4

Publication series

Name	Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009

Other

Other	2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009
Country/Territory	United States
City	Washington, DC
Period	09/11/1 → 09/11/4

All Science Journal Classification (ASJC) codes

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.1109/BIBMW.2009.5332134

Cite this

Bleik, S., Song, M., Smalter, A., Huan, J., & Lushington, G. (2009). CGM: A biomedical text categorization approach using concept graph mining. In Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009 (pp. 38-43). Article 5332134 (Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009). https://doi.org/10.1109/BIBMW.2009.5332134

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title = "CGM: A biomedical text categorization approach using concept graph mining",

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.",

author = "Said Bleik and Min Song and Aaron Smalter and Jun Huan and Gerald Lushington",

year = "2009",

doi = "10.1109/BIBMW.2009.5332134",

language = "English",

isbn = "9781424451210",

series = "Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009",

pages = "38--43",

booktitle = "Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009",

note = "2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009 ; Conference date: 01-11-2009 Through 04-11-2009",

}

Bleik, S, Song, M, Smalter, A, Huan, J & Lushington, G 2009, CGM: A biomedical text categorization approach using concept graph mining. in Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009., 5332134, Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009, pp. 38-43, 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009, Washington, DC, United States, 09/11/1. https://doi.org/10.1109/BIBMW.2009.5332134

CGM: A biomedical text categorization approach using concept graph mining. / Bleik, Said; Song, Min; Smalter, Aaron et al.
Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009. 2009. p. 38-43 5332134 (Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - CGM

T2 - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009

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AU - Song, Min

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AU - Huan, Jun

AU - Lushington, Gerald

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Y1 - 2009

N2 - 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.

AB - 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.

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M3 - Conference contribution

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T3 - Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009

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BT - Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009

Y2 - 1 November 2009 through 4 November 2009

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Bleik S, Song M, Smalter A, Huan J, Lushington G. CGM: A biomedical text categorization approach using concept graph mining. In Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009. 2009. p. 38-43. 5332134. (Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009). doi: 10.1109/BIBMW.2009.5332134

CGM: A biomedical text categorization approach using concept graph mining

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