Abstract
In this paper, we propose a method to improve the precision of top retrieved documents by re-ordering the retrieved documents in the initial retrieval. To re-order the documents, we first automatically extract key terms from top N (N<=30) retrieved documents, then we collect key terms that occur in query and their document frequencies in top N retrieved documents, finally we use these collected terms to re-order the initially retrieved documents. Each collected term is assigned a weight by its length and its document frequency in top N retrieved documents. Each document is re-ranked by the sum of weights of collected terms it contains. In our experiments on 42 query topics in NTCIR3 Cross Lingual Information Retrieval (CLIR) dataset, an average 17.8%-27.5% improvement can be made for top 10 documents and an average 6.6%-12% improvement can be made for top 100 documents at relax/rigid relevance judgment and different parameter setting.
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© 2005 Springer-Verlag Berlin Heidelberg
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Lingpeng, Y., Donghong, J., Yu, N., Guodong, Z. (2005). Document Re-ordering Based on Key Terms in Top Retrieved Documents. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2005. Lecture Notes in Computer Science, vol 3406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30586-6_61
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DOI: https://doi.org/10.1007/978-3-540-30586-6_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24523-0
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