Investigating the causal relation in informative texts

Datos Bibliográficos

ID20395154
AutoresCaroline Barrière (autor de correspondencia)
Año2001
Volumen7
Número2
Páginas135-154
Fecha de publicación2001-12-31
Open AccessNo
TipoARTICLE
RevistaTerminology International Journal of Theoretical and Applied Issues in Specialized Communication (JOURNAL)
Identificadores de la revistaISSN: 0929-9971 • E-ISSN: 1569-9994
EditorialJohn Benjamins Publishing Company (PUBLISHER • NL)
DOI10.1075/term.7.2.02bar
OpenAlexW2024075566
IdiomaEN
Citas recibidas5

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

Causal analysis · Causal model · Data mining · Econometrics · Linguistics · Natural language processing · Relationship extraction · Statistics · Advanced Text Analysis Techniques · Computer Science · Mathematics · Natural Language Processing Techniques · Philosophy · Semantic Web and Ontologies · Artificial Intelligence

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