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AAANE: Attention-Based Adversarial Autoencoder for Multi-scale Network Embedding Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Extracting Keyphrases from Research Papers Using Word Embeddings

Authors : Wei Fan, Huan Liu, Suge Wang, Yuxiang Zhang, Yaocheng Chang

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

Unsupervised random-walk keyphrase extraction models mainly rely on global structural information of the word graph, with nodes representing candidate words and edges capturing the co-occurrence information between candidate words. However, integrating different types of useful information into the representation learning process to help better extract keyphrases is relatively unexplored. In this paper, we propose a random-walk method to extract keyphrases using word embeddings. Specifically, we first design a new word embedding learning model to integrate local context information of the word graph (i.e., the local word collocation patterns) with some crucial features of candidate words and edges. Then, a novel random-walk ranking model is designed to extract keyphrases by leveraging such word embeddings. Experimental results show that our approach outperforms 8 state-of-the-art unsupervised methods on two real datasets consistently for keyphrase extraction.

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previous chapter Selective Training: A Strategy for Fast Backpropagation on Sentence Embeddings
next chapter Sequential Embedding Induced Text Clustering, a Non-parametric Bayesian Approach
Footnotes
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Metadata
Title
Extracting Keyphrases from Research Papers Using Word Embeddings
Authors
Wei Fan
Huan Liu
Suge Wang
Yuxiang Zhang
Yaocheng Chang
Copyright Year
2019
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-3-030-16141-5

Electronic ISBN: 978-3-030-16142-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-16142-2_5

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