2016/06/23 by Ju Yang, Yang Ju, Jiancong Tong +12 · 2 citations
Computer Science · #Advanced Text Analysis Techniques #Data Management and Algorithms #Information Retrieval and Search Behavior #cs.IR
paper · pdf · doi:10.48550/arxiv.1606.07188
arxiv created 2016/06/23 · arxiv updated 2016/06/24
When two terms occur together in a document, the probability of a close relationship between them and the document itself is greater if they are in nearby positions. However, ranking functions including term proximity (TP) require larger indexes than traditional document-level indexing, which slows down query processing. Previous studies also show that this technique is not effective for all types of queries. Here we propose a document ranking model which decides for which queries it would be beneficial to use a proximity-based ranking, based on a collection of features of the query. We use a machine learning approach in determining whether utilizing TP will be beneficial. Experiments show that the proposed model returns improved rankings while also reducing the overhead incurred as a result of using TP statistics.