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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (1): 38-45    DOI: 10.11925/infotech.2096-3467.2018.1352
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Extracting Fine-grained Knowledge Units from Texts with Deep Learning
Li Yu1,3,Li Qian1,2(),Changlei Fu1,Huaming Zhao1
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
2Department of Library, Information and Achieve Management, University of Chinese Academy of Sciences, Beijing 100190, China
3State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China
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[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.

Key wordsKnowledge Unit Extraction      Named Entity Recognition      Deep Learning      Bootstrapping      LSTM-CRF     
Received: 02 December 2018      Published: 04 March 2019

Cite this article:

Li Yu,Li Qian,Changlei Fu,Huaming Zhao. Extracting Fine-grained Knowledge Units from Texts with Deep Learning. Data Analysis and Knowledge Discovery, 2019, 3(1): 38-45.

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