Academic Activities Transaction Extraction Based on Deep Belief Network
Joint Authors
Zhang, Chengyuan
Wang, Xiangqian
Huang, Fang
Wan, Wencong
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-12
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
Extracting information about academic activity transactions from unstructured documents is a key problem in the analysis of academic behaviors of researchers.
The academic activities transaction includes five elements: person, activities, objects, attributes, and time phrases.
The traditional method of information extraction is to extract shallow text features and then to recognize advanced features from text with supervision.
Since the information processing of different levels is completed in steps, the error generated from various steps will be accumulated and affect the accuracy of final results.
However, because Deep Belief Network (DBN) model has the ability to automatically unsupervise learning of the advanced features from shallow text features, the model is employed to extract the academic activities transaction.
In addition, we use character-based feature to describe the raw features of named entities of academic activity, so as to improve the accuracy of named entity recognition.
In this paper, the accuracy of the academic activities extraction is compared by using character-based feature vector and word-based feature vector to express the text features, respectively, and with the traditional text information extraction based on Conditional Random Fields.
The results show that DBN model is more effective for the extraction of academic activities transaction information.
American Psychological Association (APA)
Wang, Xiangqian& Huang, Fang& Wan, Wencong& Zhang, Chengyuan. 2017. Academic Activities Transaction Extraction Based on Deep Belief Network. Advances in Multimedia،Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1122349
Modern Language Association (MLA)
Wang, Xiangqian…[et al.]. Academic Activities Transaction Extraction Based on Deep Belief Network. Advances in Multimedia No. 2017 (2017), pp.1-7.
https://search.emarefa.net/detail/BIM-1122349
American Medical Association (AMA)
Wang, Xiangqian& Huang, Fang& Wan, Wencong& Zhang, Chengyuan. Academic Activities Transaction Extraction Based on Deep Belief Network. Advances in Multimedia. 2017. Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1122349
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1122349