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Communication Dans Un Congrès Année : 2022

Citation Context Classification: Critical vs Non-critical

Résumé

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.
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Dates et versions

hal-03820225 , version 1 (18-10-2022)

Identifiants

  • HAL Id : hal-03820225 , version 1

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Sonita Te, Amira Barhoumi, Martin Lentschat, Frédérique Bordignon, Cyril Labbé, et al.. Citation Context Classification: Critical vs Non-critical. Scholarly Document Processing, Oct 2022, Gyeongju, South Korea. ⟨hal-03820225⟩
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