Selective Term Proximity Scoring Via BP-ANN
Abstract
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.
Cite
@article{arxiv.1606.07188,
title = {Selective Term Proximity Scoring Via BP-ANN},
author = {Ju Yang and Jiancong Tong and Rebecca J. Stones and Zhaohua Zhang and Benjun Ye and Gang Wang and Xiaoguang Liu},
journal= {arXiv preprint arXiv:1606.07188},
year = {2016}
}