Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation

ECIR 2020  ·  Tarek Saier, Michael Färber ·

New research is being published at a rate, at which it is infeasible for many scholars to read and assess everything possibly relevant to their work. In pursuit of a remedy, efforts towards automated processing of publications, like semantic modelling of papers to facilitate their digital handling, and the development of information filtering systems, are an active area of research. In this paper, we investigate the benefits of semantically modelling citation contexts for the purpose of citation recommendation. For this, we develop semantic models of citation contexts based on entities and claim structures. To assess the effectiveness and conceptual soundness of our models, we perform a large offline evaluation on several data sets and furthermore conduct a user study. Our findings show that the models can outperform a non-semantic baseline model and do, indeed, capture the kind of information they’re conceptualized for.

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Citation Recommendation ACL ARC citation contexts with DBLP ID claim+BoW nDCG@10 0.239 # 1
Citation Recommendation Microsoft Academic Graph citation contexts from English CS papers with abstract claim+BoW nDCG@10 0.327 # 1
Citation Recommendation RefSeer no NULL entries claim+BoW nDCG@10 0.221 # 1


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