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Attention-Based Personalized Encoder-Decoder Model for Local Citation Recommendation
- Source :
- Computational Intelligence and Neuroscience, Computational Intelligence and Neuroscience, Vol 2019 (2019)
- Publication Year :
- 2019
- Publisher :
- Hindawi, 2019.
-
Abstract
- 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.
- Subjects :
- Data Analysis
General Computer Science
Machine translation
Article Subject
Computer science
General Mathematics
02 engineering and technology
Space (commercial competition)
lcsh:Computer applications to medicine. Medical informatics
computer.software_genre
lcsh:RC321-571
Code segment
0202 electrical engineering, electronic engineering, information engineering
Humans
Attention
Layer (object-oriented design)
Precision Medicine
lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry
Information retrieval
General Neuroscience
05 social sciences
General Medicine
Softmax function
lcsh:R858-859.7
020201 artificial intelligence & image processing
0509 other social sciences
050904 information & library sciences
Construct (philosophy)
Citation
computer
Encoder
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 16875273 and 16875265
- Volume :
- 2019
- Database :
- OpenAIRE
- Journal :
- Computational Intelligence and Neuroscience
- Accession number :
- edsair.doi.dedup.....92e0900994fd041f53cf03d2742c31a8