基于词嵌入语义异常的跨学科研究内容发现方法

doi:10.11871/jfdc.issn.2096-742X.2021.06.004

数据与计算发展前沿 ›› 2021, Vol. 3 ›› Issue (6): 50-59.

doi: 10.11871/jfdc.issn.2096-742X.2021.06.004

• 专刊:科学大数据挖掘与知识发现 • 上一篇下一篇

基于词嵌入语义异常的跨学科研究内容发现方法

何涛^1,^*(),王桂芳²(),马廷灿²()

1. 海军工程大学信息安全系,湖北武汉 430033
2. 中国科学院武汉文献情报中心,湖北武汉 430071

收稿日期:2021-10-26 出版日期:2021-12-20 发布日期:2022-01-26
通讯作者: 何涛
作者简介:何涛, 海军工程大学信息安全系,博士,副研究馆员,主要研究方向为自然语言处理、科学计量。
本文中负责提出思路、实验设计。
HE Tao, Ph.D, is an associate rese-arch librarian of Department of Infor-mation Security, Naval University of Engineering. His main research interests include natural language processing and scientometrics.
In this paper, he is responsible for the design of method and experiment. E-mail: taohe@whu.edu.cn;|王桂芳,中国科学院武汉文献情报中心,博士,副研究馆员,主要研究方向为学科领域技术机会识别。
本文中负责数据分析。
WANG Guifang, Ph.D, is an associate research librarian at Wuhan Library, Chi-nese Academy of Sciences. Her current research interests inclu-de identification of technical opportunities in disciplines.
In this paper, she is responsible for data analysis. E-mail: conawang0206@gmail.com;|马廷灿,中国科学院武汉文献情报中心,硕士生导师,研究馆员,主要研究方向为科学计量学与应用。
本文中负责研究指导。
MA Tingcan is a research librarian and master supervisor at Wuhan Library, Chinese Academy of Sciences. Hiscurrent research interests include scientometrics and informetrics.
He is responsible for the research guidance. E-mail: matc@whlib.ac.cn
基金资助:
中国科学院人才项目“青年创新促进会”(2016160)

Discovering Interdisciplinary Research Based on Word Embedding

HE Tao^1,^*(),WANG Guifang²(),MA Tingcan²()

1. Department of Information Security, Naval University of Engineering, Wuhan, Hubei 430033, China
2. Wuhan Library, Chinese Academy of Sciences, Wuhan, Hubei 430071, China

Received:2021-10-26 Online:2021-12-20 Published:2022-01-26
Contact: HE Tao

摘要/Abstract

摘要：

【目的】跨学科的研究内容推动了科学重大发现的产生,科研人员需要了解其研究方向中所出现的跨学科研究内容。科学文献规模变得越来越庞大,采用人工阅读的方式从科学文献中寻找跨学科的研究内容变得越来越困难,需要计算机辅助科研人员对跨学科研究内容进行揭示。【方法】本文提出了一种跨学科研究内容的自动识别方法,此方法利用人工智能中词嵌入语义分布的特性,在构建的约170万自然科学常见词汇的词嵌入的基础上,通过让计算机从论文的作者关键词中自动识别出语义异常的词汇来发现跨学科的研究内容。【结果】将该方法应用于深度学习研究方向,挖掘出了若干跨学科的自然科学研究内容。【局限】由于传统词嵌入在表示一词多意上的缺陷,语义异常作者关键词识别的准确性有待提升。【结论】本文的方法为跨学科研究内容的发现提供了一种新的解决思路。

关键词: 跨学科研究, 词嵌入, 语义异常, 深度学习

Abstract:

[Objective] Interdisciplinary research has promoted the emergence of many major scientific discoveries, hence researchers need to identify interdisciplinary problems in their research field. Since huge number of scientific papers have been published today, it is difficult to discover interdisciplinary research manually, automatic searching methods are needed.[Methods] This paper proposes an approach to discover interdisciplinary research automatically. This method adopts the word embedding mechanism in artificial intelligence, which covers approximately 1.7 million vocabularies of natural sciences. It captures interdisciplinary research by automatically identifying the keywords with semantic anomalies from the author's keywords. [Results] The method is applied to the research field of deep learning and discovers some interdisciplinary natural science research. [L-imitations] Due to the shortcomings of traditional word embedding in representing the word with multiple meanings, the recognition accuracy of keywords with semantic anomalies needs to be improved. [Conclusions] The proposed method is a novel solution to the discovery of interdisciplinary research.

Key words: interdisciplinary research, word embedding, semantic anomaly, deep learning

何涛,王桂芳,马廷灿. 基于词嵌入语义异常的跨学科研究内容发现方法[J]. 数据与计算发展前沿, 2021, 3(6): 50-59.

HE Tao,WANG Guifang,MA Tingcan. Discovering Interdisciplinary Research Based on Word Embedding[J]. Frontiers of Data and Computing, 2021, 3(6): 50-59.

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	常见于深度学习的研究论文	含有跨学科研究内容的语义	作者关键词
异常程度最高的10个作者关键词	否	是	A: “rice seed”, “food recognition”, “optical music recognition”, “hot deformation”, “rna-binding protein”, “froth flotation”, “cluster finding”
	否	否	B: “grand challenge”
	是	否	C: “network compression”, “trigger detection”
异常程度最低的10个作者关键词	是	否	“image classification”, “feature representation”, “feature extraction”, “image representation”, “human action recognition”, “deep learning”, “feature fusion”, “computer vision”, “image recognition”, “discriminative features”

	文章标题	异常关键词	跨学科研究内容
1	Variety identification of single rice seed using hyperspectral imaging combined with convolutional neural network.	rice seed	在食品科学中使用深度学习对水稻纯度进行研究。
2	Hot deformation characteristics and dislocation substructure evolution of a nickel-base alloy considering effects of delta phase.	hot deformation	在冶金领域使用深度学习对热变形进行研究。
3	UV-flashover evaluation of porcelain insulators based on deep learning.	ultraviolet spectra	在电力传输中使用深度学习进行器件的绝缘特性研究。
4	In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks.	selective laser melting	在金属部件制作中使用深度学习对选择性激光断融进行研究。
5	Image-based deep learning for classification of noise tran-sients in gravitational wave detectors.	gravitational waves	在引力波侦测中使用深度学习对噪声进行分类。

基于词嵌入语义异常的跨学科研究内容发现方法

Discovering Interdisciplinary Research Based on Word Embedding

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