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 |
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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)