Improving Retrieval Effectiveness by Using Key Terms in Top Retrieved Documents

In this paper, we propose a method to improve the precision of top retrieved documents in Chinese information retrieval where the query is a short description by re-ordering retrieved documents in the initial retrieval. To re-order the documents, we firstly find out terms in query and their importance scales by making use of the information derived from top N(N<=30) retrieved documents in the initial retrieval; secondly, we re-order retrieved K(N<<K) documents by what kinds of terms of query they contain. That is, we first automatically extract key terms from top N retrieved documents, then we collect key terms that occur in query and their document frequencies in the 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%-26.9% improvement can be made for top 100 documents at relax/rigid relevance judgment and different parameter setting.