Identifying relevant full-text articles for database curation

Chih Lee*, Wen Juan Hou, Hsin His Chen

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


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 languageEnglish
JournalNIST Special Publication
Publication statusPublished - 2005
Externally publishedYes
Event14th Text REtrieval Conference, TREC 2005 - Gaithersburg, MD, United States
Duration: 2005 Nov 152005 Nov 18

ASJC Scopus subject areas

  • General Engineering


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