Identifying relevant full-text articles for database curation

Chih Lee; Wen-Juan Hou; Hsin His Chen

Identifying relevant full-text articles for database curation

Chih Lee, Wen-Juan Hou, Hsin His Chen

Department of Computer Science and Information Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

In this paper, we propose an approach for identifying curatable articles from a large pool. Our system currently considers three parts of an article as three individual representations of the article, and utilizes two domain-specific resources to reveal the deep knowledge contained in the article in order to generate more representations of the article. Cross-validation is employed to find the best combination of representations and an SVM classifier is trained out of this combination. The cross-validation results and results of the official runs are listed. The experimental results show overall high performance.

Original language	English
Journal	NIST Special Publication
Publication status	Published - 2005 Dec 1
Event	14th Text REtrieval Conference, TREC 2005 - Gaithersburg, MD, United States Duration: 2005 Nov 15 → 2005 Nov 18

ASJC Scopus subject areas

Engineering(all)

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title = "Identifying relevant full-text articles for database curation",

abstract = "In this paper, we propose an approach for identifying curatable articles from a large pool. Our system currently considers three parts of an article as three individual representations of the article, and utilizes two domain-specific resources to reveal the deep knowledge contained in the article in order to generate more representations of the article. Cross-validation is employed to find the best combination of representations and an SVM classifier is trained out of this combination. The cross-validation results and results of the official runs are listed. The experimental results show overall high performance.",

author = "Chih Lee and Wen-Juan Hou and Chen, {Hsin His}",

year = "2005",

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language = "English",

journal = "NIST Special Publication",

issn = "1048-776X",

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AU - Hou, Wen-Juan

AU - Chen, Hsin His

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N2 - In this paper, we propose an approach for identifying curatable articles from a large pool. Our system currently considers three parts of an article as three individual representations of the article, and utilizes two domain-specific resources to reveal the deep knowledge contained in the article in order to generate more representations of the article. Cross-validation is employed to find the best combination of representations and an SVM classifier is trained out of this combination. The cross-validation results and results of the official runs are listed. The experimental results show overall high performance.

AB - In this paper, we propose an approach for identifying curatable articles from a large pool. Our system currently considers three parts of an article as three individual representations of the article, and utilizes two domain-specific resources to reveal the deep knowledge contained in the article in order to generate more representations of the article. Cross-validation is employed to find the best combination of representations and an SVM classifier is trained out of this combination. The cross-validation results and results of the official runs are listed. The experimental results show overall high performance.

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M3 - Conference article

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T2 - 14th Text REtrieval Conference, TREC 2005

Y2 - 15 November 2005 through 18 November 2005

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