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Open Access

ARTICLE

Information Classification and Extraction on Official Web Pages of Organizations

Jinlin Wang1, Xing Wang1, *, Hongli Zhang1, Binxing Fang1, Yuchen Yang1, Jianan Liu2
1 School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150006, China.
2 China Electronic Equipment System Engineering Company, Beijing, 100039, China
* Corresponding Author: Xing Wang. Email: .

Computers, Materials & Continua 2020, 64(3), 2057-2073. https://doi.org/10.32604/cmc.2020.011158

Received 30 April 2020; Accepted 15 May 2020; Issue published 30 June 2020

Abstract

As a real-time and authoritative source, the official Web pages of organizations contain a large amount of information. The diversity of Web content and format makes it essential for pre-processing to get the unified attributed data, which has the value of organizational analysis and mining. The existing research on dealing with multiple Web scenarios and accuracy performance is insufficient. This paper aims to propose a method to transform organizational official Web pages into the data with attributes. After locating the active blocks in the Web pages, the structural and content features are proposed to classify information with the specific model. The extraction methods based on trigger lexicon and LSTM (Long Short-Term Memory) are proposed, which efficiently process the classified information and extract data that matches the attributes. Finally, an accurate and efficient method to classify and extract information from organizational official Web pages is formed. Experimental results show that our approach improves the performing indicators and exceeds the level of state of the art on real data set from organizational official Web pages.

Keywords

Web pre-process, feature classification, data extraction, trigger lexicon, LSTM.

Cite This Article

J. Wang, X. Wang, H. Zhang, B. Fang, Y. Yang et al., "Information classification and extraction on official web pages of organizations," Computers, Materials & Continua, vol. 64, no.3, pp. 2057–2073, 2020.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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