| |
| With the tremendous growth in the number of published academic papers, researchers find that it is a time-consuming task to search suitable references while writing scientific papers. To solve this problem, citation recommendation is proposed to recommend a list of candidate papers which are most relevant to the given manuscript. In this paper, we propose a context-aware citation recommendation approach based on BERT and side information. The proposed approach simultaneously incorporates sentence-level representation extracted by BERT from papers and network-based representation extracted by Node2Vec with joint-information (papers, authors and venues). Besides that, side information is taken into account when calculating the relevance between candidate papers and the target manuscript. By introducing the latest natural language processing algorithm and extracting rich features manually, the proposed approach performs well in precision and recall. When conducting experiment on AAN dataset, the results demonstrate the effectiveness of the proposed approach to optimize the quality of citation recommendation, compared with other baseline approaches. |
|
| Keywords:Technology of Computer Application; Citation Recommendation; Graph Embedding; Neural Network
|
|
