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Erschienen in:
Neural or Statistical: An Empirical Study on Language Models for Chinese Input Recommendation on Mobile Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

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

verfasst von : Wenyu Zhao, Dong Zhou

Erschienen in: Information Retrieval

Verlag: 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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Vorheriges Kapitel Tripartite-Replicated Softmax Model for Document Representations
Nächstes Kapitel Constructing Semantic Hierarchies via Fusion Learning Architecture
Fußnoten
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Metadaten
Titel
A Normalized Framework Based on Multiple Relationships for Document Re-ranking
verfasst von
Wenyu Zhao
Dong Zhou
Copyright-Jahr
2017
Buch
Information Retrieval

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

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

Copyright-Jahr: 2017

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

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