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Neural or Statistical: An Empirical Study on Language Models for Chinese Input Recommendation on Mobile Read chapter Read first chapter

2017 | OriginalPaper | Chapter

A Normalized Framework Based on Multiple Relationships for Document Re-ranking

Authors : Wenyu Zhao, Dong Zhou

Published in: Information Retrieval

Publisher: Springer International Publishing

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Abstract

Document re-ranking has been widely adopted in Information Retrieval as a way of improving precision of top documents based on the first round retrieval results. There are methods that use semi-supervised learning based on graphs constructed based on similarities between documents. However, most of them only consider relationships between documents. In this paper, we propose an approach to take the relationships between documents, between words in documents, as well as between documents and words into consideration. We develop a novel generative model which integrates neural language model with latent semantic model, then we incorporate the relationships between documents and words into a normalized framework to re-rank documents based on the initial retrieval results. Experimental results show that the method show significant improvements in comparison with other baseline methods.

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previous chapter Tripartite-Replicated Softmax Model for Document Representations
next chapter Constructing Semantic Hierarchies via Fusion Learning Architecture
Footnotes
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Metadata
Title
A Normalized Framework Based on Multiple Relationships for Document Re-ranking
Authors
Wenyu Zhao
Dong Zhou
Copyright Year
2017
Book
Information Retrieval

Print ISBN: 978-3-319-68698-1

Electronic ISBN: 978-3-319-68699-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-68699-8_10