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Communication Dans Un Congrès Année : 2022

GraphCite: Citation Intent Classification in Scientific Publications via Graph Embeddings

Résumé

Citations are crucial in scientific works as they help position a new publication. Each citation carries a particular intent, for example, to highlight the importance of a problem or to compare against results provided by another method. The authors' intent when making a new citation has been studied to understand the evolution of a field over time or to make recommendations for further citations. In this work, we address the task of citation intent prediction from a new perspective. In addition to textual clues present in the citation phrase, we also consider the citation graph, leveraging high-level information of citation patterns. In this novel setting, we perform a thorough experimental evaluation of graph-based models for intent prediction. We show that our model, GraphCite, improves significantly upon models that take into consideration only the citation phrase. Our code is available online.
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Dates et versions

hal-03648498 , version 1 (21-04-2022)

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  • HAL Id : hal-03648498 , version 1

Citer

Dan Berrebbi, Nicolas Huynh, Oana Balalau. GraphCite: Citation Intent Classification in Scientific Publications via Graph Embeddings. 2nd International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment, Apr 2022, Lyon / Virtual, France. ⟨hal-03648498⟩
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