TY  - GEN
Y1  - 2025///
KW  - Collocation Analysis
KW  -  Statistical Significance
KW  - Culinary Verbs
TI  - Modeling Lexical Fields for Translation: a Corpus-Based Study of Armenian, German, and English Culinary Verbs
AV  - public
N2  - This doctoral thesis presents a corpus-based, bottom-up methodology for analyzing culinary lexical fields in Armenian, German, and English using statistical and computational tools combined with manual semantic annotation. Drawing from authentic comparable corpora, the study develops an interpretable model for identifying translation equivalents and typical lexical choices across languages, focusing on culinary verbs. The methodology offers practical applications for language learners, educators, translators, and developers of machine translation (MT) systems by minimizing atypical collocational usage and enhancing translation accuracy. A key innovation lies in the interpretability of the translation and lexical models, addressing a major shortcoming in current neural MT systems, which often lack transparency in their output. The proposed framework enables explainable lexical modeling and supports the evaluation and refinement of both dictionary definitions and MT outputs. While tested primarily on three languages, the model has demonstrated scalability, with preliminary success in French, and can be adapted to other languages with minor adjustments. By integrating manual semantic analysis with data-driven techniques, this work bridges the gap between logic-based and statistical approaches to language processing, offering a meaningful step toward explainable and semantically grounded applications in NLP and AI.
CY  - Heidelberg
ID  - heidok36999
A1  - Dallakyan, Meri
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/36999/
ER  -