Attention-Based Personalized Encoder-Decoder Model for Local Citation Recommendation
Joint Authors
Yang, Libin
Zhang, Zeqing
Cai, Xiaoyan
Dai, Tao
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-03
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
With a tremendous growth in the number of scientific papers, researchers have to spend too much time and struggle to find the appropriate papers they are looking for.
Local citation recommendation that provides a list of references based on a text segment could alleviate the problem.
Most existing local citation recommendation approaches concentrate on how to narrow the semantic difference between the scientific papers’ and citation context’s text content, completely neglecting other information.
Inspired by the successful use of the encoder-decoder framework in machine translation, we develop an attention-based encoder-decoder (AED) model for local citation recommendation.
The proposed AED model integrates venue information and author information in attention mechanism and learns relations between variable-length texts of the two text objects, i.e., citation contexts and scientific papers.
Specifically, we first construct an encoder to represent a citation context as a vector in a low-dimensional space; after that, we construct an attention mechanism integrating venue information and author information and use RNN to construct a decoder, then we map the decoder’s output into a softmax layer, and score the scientific papers.
Finally, we select papers which have high scores and generate a recommended reference paper list.
We conduct experiments on the DBLP and ACL Anthology Network (AAN) datasets, and the results illustrate that the performance of the proposed approach is better than the other three state-of-the-art approaches.
American Psychological Association (APA)
Yang, Libin& Zhang, Zeqing& Cai, Xiaoyan& Dai, Tao. 2019. Attention-Based Personalized Encoder-Decoder Model for Local Citation Recommendation. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1129315
Modern Language Association (MLA)
Yang, Libin…[et al.]. Attention-Based Personalized Encoder-Decoder Model for Local Citation Recommendation. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-7.
https://search.emarefa.net/detail/BIM-1129315
American Medical Association (AMA)
Yang, Libin& Zhang, Zeqing& Cai, Xiaoyan& Dai, Tao. Attention-Based Personalized Encoder-Decoder Model for Local Citation Recommendation. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1129315
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129315