Frédérique Bordignon


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Citation Context Classification: Critical vs Non-critical
Sonita Te | Amira Barhoumi | Martin Lentschat | Frédérique Bordignon | Cyril Labbé | François Portet
Proceedings of the Third Workshop on Scholarly Document Processing

Recently, there have been numerous research in Natural Language Processing on citation analysis in scientific literature. Studies of citation behavior aim at finding how researchers cited a paper in their work. In this paper, we are interested in identifying cited papers that are criticized. Recent research introduces the concept of Critical citations which provides a useful theoretical framework, making criticism an important part of scientific progress. Indeed, identifying critics could be a way to spot errors and thus encourage self-correction of science. In this work, we investigate how to automatically classify the critical citation contexts using Natural Language Processing (NLP). Our classification task consists of predicting critical or non-critical labels for citation contexts. For this, we experiment and compare different methods, including rule-based and machine learning methods, to classify critical vs. non-critical citation contexts. Our experiments show that fine-tuning pretrained transformer model RoBERTa achieved the highest performance among all systems.