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Article published In:
Terminology
Vol. 28:1 (2022) ► pp.129–156

Framing karstology

From definitions to knowledge structures and automatic frame population

ORCID logo | University of Ljubljana
| Jožef Stefan Institute

We describe the creation of a knowledge base in the field of karstology using the frame-based approach. Apart from providing a new multilingual resource using manually annotated definitions as the source of structured information, the main focus is on exploring text mining methods to identify targeted knowledge structures in specialised corpora. The first stage of this process is the design of a domain model and its implementation in a definition annotation task. Once annotation is completed, an analysis of typical co-occurrence patterns between semantic categories and the relations describing them allows us to discern ideal definition templates. We demonstrate that such templates contribute to a more comprehensive and structured representations of concepts, but also help us design targeted text mining experiments to retrieve new semantic relations from text. Two such experiments are presented, the first using intersections of word embeddings to identify words expressing a specific semantic relation, and the second using the embedding of the semantic relation to extract multiword units which contain the target relation. Results suggest that the proposed methods are promising for capturing the semantic properties of relations in frame-based knowledge modelling.

Keywords: frame-based terminology, word embeddings, semantic relations, karstology
Article outline
  • 1.Introduction
  • 2.Related work
  • 3.Building the TermFrame knowledge base
    • 3.1The domain model
    • 3.2Extracting definitions
    • 3.3Multi-layered semantic annotation
    • 3.4Eliciting frames
  • 4.Towards automatic frame population
    • 4.1Extracting relations using intersections of word embeddings
    • 4.2Targeted hyponym extraction
  • Conclusions
  • Acknowledgements
  • Notes
  • References
Published online: 27 January 2022
DOI logo
https://doi.org/10.1075/term.21005.vin
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