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- Volume 28, Issue 2, 2022
Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication - Volume 28, Issue 2, 2022
Volume 28, Issue 2, 2022
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Interlingual terminological asymmetry as one of the aspects of studying foreign languages
Author(s): Tetyana Karlovapp.: 199–227 (29)More LessAbstractThe purpose of the study is to explore interlingual terminological asymmetry from the cognitive-onomasiological standpoints. False synonymy of adjectives in anatomical terminology of Latin, Ukrainian, Russian, and English have been analyzed and interpreted as factors causing interlingual terminological asymmetry.
In Latin anatomical terminology, there is a significant number of nominative units with similar meanings. They often have one equivalent in other (modern) languages or can be simply confused as a result of misunderstanding. It creates difficulties in the process of interlingual terminological communication. Despite the substrate nature of the Latin anatomical terminology, national terminological systems undergo different types of correlations in their functioning. The author assumes such correlations are related to the concepts of “terminological asymmetry” (lack of interlingual interchangeability of terms) and “quasi-synonymous effect” (the loss of cognitive-differential function of the term).
Attention is also paid to the preparation of a theoretical basis for creating a special thesaurus to help speakers of Ukrainian study medical terminology in Latin and English.
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Corpus-based bilingual terminology extraction in the power engineering domain
Author(s): Tanja Ivanović, Ranka Stanković, Branislava Šandrih Todorović and Cvetana Krstevpp.: 228–263 (36)More LessAbstractThis paper presents the resources and tools used to extract and evaluate bilingual, English-Serbian terminology in the power engineering domain. The resources consist of existing general and domain lexica, and a domain parallel corpus; tools include term extractors for both languages and a tool for aligning the segments belonging to corpus sentences. The system was tested by varying a match function that establishes the presence of an extracted term in an aligned segment (a chunk), ranging from very loose to strict. The evaluation of results showed that the precision of English term extraction was 92%, Serbian term extraction 86%, while the precision of bilingual pair extraction was 72% based on the strictest match function. The result of extraction was 2,684 correct bilingual pairs that enhanced the terminology database and can further be used to support the search of the power engineering aligned collection stored in a digital library.
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Repérage automatisé de l’hyponymie dans des corpus spécialisés en français à l’aide de Sketch Engine
Author(s): Antonio San Martín, Catherine Trekker and Pilar León-Araúzpp.: 264–298 (35)More LessAbstractHyponymy is an essential semantic relation in terminology, as it represents the hierarchical organization of concepts. Much has been written about hyponymy extraction. However, terminologists working with French do not currently have user-friendly and freely available tools to automatically extract hyper-hyponymic pairs from their own corpora. This paper presents the most recent version of the ESSG (EcoLexicon Semantic Sketch Grammar) methodology, a knowledge-pattern-based approach that enables Sketch Engine to extract semantic relations. This methodology is applied to the development and evaluation of the ESSG-fr, a semantic sketch grammar for hyponymy extraction in French. The evaluation results show that the ESSG-fr is a reliable domain-independent tool for terminologists wishing to extract simple hyper-hyponymic pairs and the corresponding concordances from specialized corpora.
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Automatic medical term extraction from Vietnamese clinical texts
Author(s): Chau Vo, Tru Cao, Ngoc Truong, Trung Ngo and Dai Buipp.: 299–327 (29)More LessAbstractIn this paper, we propose the first method for automatic Vietnamese medical term discovery and extraction from clinical texts. The method combines linguistic filtering based on our defined open patterns with nested term extraction and statistical ranking using C-value. It does not require annotated corpora, external data resources, parameter settings, or term length restriction. Beside its specialty in handling Vietnamese medical terms, another novelty is that it uses Pointwise Mutual Information to split nested terms and the disjunctive acceptance condition to extract them. Evaluated on real Vietnamese electronic medical records, it achieves a precision of about 74% and recall of about 92% and is proved stably effective with small datasets. It outperforms the previous works in the same category of not using annotated corpora and external data resources. Our method and empirical evaluation analysis can lay a foundation for further research and development in Vietnamese medical term discovery and extraction.
Volumes & issues
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Volume 30 (2024)
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Volume 29 (2023)
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Volume 28 (2022)
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Volume 27 (2021)
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Volume 26 (2020)
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Volume 25 (2019)
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Volume 24 (2018)
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Volume 23 (2017)
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Volume 22 (2016)
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Volume 21 (2015)
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Volume 20 (2014)
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Volume 19 (2013)
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Volume 18 (2012)
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Volume 17 (2011)
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Volume 16 (2010)
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Volume 15 (2009)
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Volume 14 (2008)
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Volume 13 (2007)
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Volume 12 (2006)
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Volume 11 (2005)
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Volume 10 (2004)
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Volume 9 (2003)
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Volume 8 (2002)
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Volume 7 (2001)
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Volume 6 (2000)
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Volume 5 (1998)
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Volume 4 (1997)
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Volume 3 (1996)
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Volume 2 (1995)
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Volume 1 (1994)
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Methods of automatic term recognition: A review
Author(s): Kyo Kageura and Bin Umino
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