Methodological Framework for the Development of an English-Lithuanian Cybersecurity Termbase
Bibliographic Data
| ID | 12865869 |
|---|---|
| Authors | Sigita Rackevičienė (0000-0001-5794-0296, Mykolas Romeris University, corresponding author), Liudmila Mockienė (0000-0001-7153-7276, Mykolas Romeris University), Andrius Utka (0000-0001-5212-4310, Vytautas Magnus University), Aivaras Rokas (Vytautas Magnus University) |
| Year | 2021 |
| Issue | 39 |
| Pages | 85-92 |
| Publication date | 2021-11-27 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Studies About Languages (JOURNAL) |
| Journal identifiers | ISSN: 1648-2824 • E-ISSN: 2029-7203 |
| Publisher | Kaunas University of Technology (KTU) (PUBLISHER) |
| DOI | 10.5755/j01.sal.1.39.29156 |
| OpenAlex | W3217195679 |
| Language | EN |
| References cited | 2 |
The aim of the paper is to present a methodological framework for the development of an English-Lithuanian bilingual termbase in the cybersecurity domain, which can be applied as a model for other language pairs and other specialised domains. It is argued that the presented methodological approach can ensure creation of high-quality bilingual termbases even with limited available resources. The paper touches upon the methods and problems of dataset (corpora) compilation, terminology annotation, automatic bilingual term extraction (BiTE) and alignment, knowledge-rich context extraction, and linguistic linked open data (LLOD) technologies. The paper presents theoretical considerations as well as the arguments on the effectiveness of the described methods. The theoretical analysis and a pilot study allow arguing that: 1) a combination of parallel and comparable corpora enable to considerably expand the amount and variety of data sources that can be used for terminology extraction; this methodology is especially important for less-resourced languages which often lack parallel data; 2) deep learning systems trained by using manually annotated data (gold standard corpora) allow effective automatization of extraction of terminological data and metadata, which enables to regularly update termbases with minimised manual input; 3) LLOD technologies enable to integrate the terminological data into the global linguistic data ecosystem and make it reusable, searchable and discoverable across the Web
Context (archaeology · Data science · Domain (mathematical analysis · Information retrieval · Linguistics · Lithuanian · Metadata · Natural language processing · Terminology · Variety (cybernetics · World Wide Web · Computer Science · linguistics and terminology studies · Natural Language Processing Techniques · Semantic Web and Ontologies · Artificial Intelligence
| Highly cited | No |
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