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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

Constructing Semantic Hierarchies via Fusion Learning Architecture

Authors : Tianwen Jiang, Ming Liu, Bing Qin, Ting Liu

Published in: Information Retrieval

Publisher: Springer International Publishing

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Abstract

Semantic hierarchies construction means to build structure of concepts linked by hypernym-hyponym (“is-a”) relations. A major challenge for this task is the automatic discovery of hypernym-hyponym (“is-a”) relations. We propose a fusion learning architecture based on word embeddings for constructing semantic hierarchies, composed of discriminative generative fusion architecture and a very simple lexical structure rule for assisting, getting an F1-score of 74.20% with 91.60% precision-value, outperforming the state-of-the-art methods on a manually labeled test dataset. Subsequently, combining our method with manually-built hierarchies can further improve F1-score to 82.01%. Besides, the fusion learning architecture is language-independent.

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Footnotes
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Metadata
Title
Constructing Semantic Hierarchies via Fusion Learning Architecture
Authors
Tianwen Jiang
Ming Liu
Bing Qin
Ting Liu
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_11