Learning contextual representations of citations via graph transformer

Abstract

This study aims at representing the citation based on the citation context extracted from the citation network. Researchers cite papers for various purposes to describe their arguments in a logical structure. Thus, citations have different roles depending on what structure they are cited in the paper. In this paper, we first present a definition of the citation context and initialize the embedding vector based on the citation order and location. Then, based on the graph transformer model, we learn contextual citation embeddings. To represent citation context, we consider the following three parts: (i) textual features of paper, (ii) positional features of the citation context, and (iii) structural features of the citation network by applying the self-attention mechanism. © 2021 CEUR-WS. All rights reserved.