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arXiv · 1606.07188

Selective Term Proximity Scoring Via BP-ANN

Ju Yang·Jiancong Tong·Rebecca J. Stones·Zhaohua Zhang·Benjun Ye·Gang Wang·Xiaoguang Liu
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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.

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Ju Yang, Jiancong Tong, Rebecca J. Stones, Zhaohua Zhang, Benjun Ye, Gang Wang, Xiaoguang Liu. 2016-06-23. Selective Term Proximity Scoring Via BP-ANN. https://arxiv.org/abs/1606.07188

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