Extracting Fine-grained Knowledge Units from Texts with Deep Learning

被引：0

作者：

Yu L. ^{[1
,3
]}

Qian L. ^{[1
,2
]}

Fu C. ^{[1
]}

Zhao H. ^{[1
]}

机构：

[1] National Science Library, Chinese Academy of Sciences, Beijing

[2] Department of Library, Information and Achieve Management, University of Chinese Academy of Sciences, Beijing

[3] State Key Laboratory of Resources and Environmental Information System, Beijing

来源：

Data Analysis and Knowledge Discovery | 2019年 / 3卷 / 01期

关键词：

Bootstrapping; Deep Learning; Knowledge Unit Extraction; LSTM-CRF; Named Entity Recognition;

D O I：

10.11925/infotech.2096-3467.2018.1352

中图分类号：

学科分类号：

摘要：

[Objective] This paper tries to extract fine-grained knowledge units from texts with a deep learning model based on the modified bootstrapping method. [Methods] First, we built the lexicon for each type of knowledge unit with the help of search engine and keywords from Elsevier. Second, we created a large annotated corpus based on the bootstrapping method. Third, we controlled the quality of annotation with the estimation models of patterns and knowledge units. Finally, we trained the proposed LSTM-CRF model with the annotated corpus, and extracted new knowledge units from texts. [Results] We retrieved four types of knowledge units (study scope, research method, experimental data, as well as evaluation criteria and their values) from 17,756 ACL papers. The average precision was 91%, which was calculated manually. [Limitations] The parameters of models were pre-defined and modified by human. More research is needed to evaluate the performance of this method with texts from other domains. [Conclusions] The proposed model effectively addresses the issue of semantic drifting. It could extract knowledge units precisely, which is an effective solution for the big data acquisition process of intelligence analysis. © 2019 Chinese Academy of Sciences. All rights reserved.

引用

页码：38 / 45

页数：7

共 27 条

[1]

Gao Jiping, Ding Kun, Pan Yuntao, Et al., A Review of Knowledge Unit Research, Information studies: Theory & Application, 38, 7, pp. 134-138, (2015)

[2]

Qian Li, Zhang Xiaolin, Wang Qian, Research Design Fingerprint Description Framework Based on Scientific Papers, Journal of Academic Libraries, 33, 1, pp. 14-20, (2015)

[3]

Liu Zeyuan, Some Thoughts on Knowledge Graph, (2010)

[4]

Zhu Qingsong, Leng Fuhai, Topic Identification of Highly Cited Papers Based on Citation Content Analysis, Journal of Library Science in China, 40, 1, pp. 39-49, (2014)

[5]

Wang Zixuan, Le Xiaoqiu, He Yuanbiao, Recognizing Core Topic Sentences with Improved TextRank Algorithm Based on WMD Semantic Similarity, Data Analysis and Knowledge Discovery, 1, 1, pp. 39-49, (2017)

[6]

Ding Heng, Lu Wei, Building Standard Literature Knowledge Service System, New Technology of Library and Information Service, 7-8, pp. 120-128, (2016)

[7]

Augenstein I, Das M, Riedel S, Et al., SemEval 2017 Task 10: ScienceIE-Extracting Keyphrases and Relations from Scientific Publications, Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), (2017)

[8]

Zeng Wen, Xu Shuo, Zhang Yunliang, Et al., The Research and Analysis on Automatic Extraction of Science and Technology Literature Terms, New Technology of Library and Information Service, 1, pp. 51-55, (2014)

[9]

Gupta S, Manning C D., Analyzing the Dynamics of Research by Extracting Key Aspects of Scientific Papers, Proceedings of the 5th International Joint Conference on Natural Language Processing, (2011)

[10]

Guo Hongmei, Kong Beibei, Zhang Zhixiong, Study on Textual Topic Identification by Clustering Clique Structure in Multi-Relationship Text Graph, Journal of the China Society for Scientific and Technical Information, 36, 5, pp. 433-442, (2017)

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