Investigating the causal relation in informative texts

Caroline Barrière

Our work investigates the causal relation as it is expressed in informative texts. We view causal relations as important because of the dynamic dimension they bring to a domain model. Thorough study of a corpus leads us to distinguish two prominent classes of indicators of the causal relation: conjunctional phrases, and verbs. This paper identifies multiple knowledge-rich patterns within each class and studies their usage, frequency and noise. Results from this manual investigation informs a discussion on the feasibility of automatic extraction of the different forms of expression of the causal relation.

Keywords: Causal relation, automatic knowledge extraction, knowledge-rich patterns, terminological knowledge base.

Published online: 22 April 2002
https://doi.org/10.1075/term.7.2.02bar

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2020. In Lexical Semantics for Terminology [Terminology and Lexicography Research and Practice, 20],

Barrière, Caroline

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Cromley, Jennifer G., Shufeng Ma, Martin Van Boekel & Aygul Parpucu Dane

2020. Pickup of Causal Language and Inference During and After Reading Scientific Text. Reading Psychology 41:3 ► pp. 157 ff.

Marshman, Elizabeth

2014. Enriching terminology resources with knowledge-rich contexts. Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication 20:2 ► pp. 225 ff.

Marshman, Elizabeth, Marie-Claude L'Homme & Victoria Surtees

2008. Portability of cause–effect relation markers across specialised domains and text genres: a comparative evaluation. Corpora 3:2 ► pp. 141 ff.

Sambre, Paul & Cornelia Wermuth

2015. In Handbook of Terminology [Handbook of Terminology, 1], ► pp. 101 ff.

Sánchez Cárdenas, Beatriz & Carlos Ramisch

2019. Eliciting specialized frames from corpora using argument-structure extraction techniques. Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication 25:1 ► pp. 1 ff.

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