Document re-ordering based on key terms in top retrieved documents

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 double left arrow 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.