Investigating the causal relation in informative texts
Bibliographic Data
| ID | 20395154 |
|---|---|
| Authors | Caroline Barrière (corresponding author) |
| Year | 2001 |
| Volume | 7 |
| Issue | 2 |
| Pages | 135-154 |
| Publication date | 2001-12-31 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Terminology International Journal of Theoretical and Applied Issues in Specialized Communication (JOURNAL) |
| Journal identifiers | ISSN: 0929-9971 • E-ISSN: 1569-9994 |
| Publisher | John Benjamins Publishing Company (PUBLISHER • NL) |
| DOI | 10.1075/term.7.2.02bar |
| OpenAlex | W2024075566 |
| Language | EN |
| Citations received | 5 |
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
Designing and evaluating patterns for relation acquisition from texts with Caméléon
Web-based extraction of semantic relation instances for terminology work
Conceptual Dynamism in Terminological Knowledge Bases
Enriching terminology resources with knowledge-rich contexts
Eliciting specialized frames from corpora using argument-structure extraction techniques
| Citations per year | 0.28 |
|---|---|
| Citation span | 2008 - 2019 (12) |
| Highly cited | No |