Inflectional Description of Bulgarian Multiword Expressions: An Analysis of Two-Component Geographical Names

This article explores the grammatical and semantic characteristics of two-component compound proper names in Bulgarian, with a particular focus on geograph-ical names, aiming to define their inflectional types and integrate them into the respec-tive dictionary entries. The study is based on lexical units automatically extracted from internet sources, including structured resources such as Wikipedia. These units were manually filtered and categorised according to the type of referent, e.g. town, village, mountain, spring, etc. As a result, the initial dataset of 4,000 candidates was reduced to approximately 2,600 entries. The selected names were automatically POS-tagged and analysed in terms of their syntactic structure, lexical and grammatical categories, inflectional properties, defectivi-ty, etc. These features collectively determine the inflectional type of each named entity. The inflectional types enable both the generation of inflected forms and their recognition in free text.

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Publication Details

Journal
Journal of Bulgarian Language
Published
2025-02-28
DOI
https://doi.org/10.47810/bl.72.25.pr.09
Primary Topic
Linguistics, Language Diversity, and Identity
Type
article
Field-Weighted Citation Impact
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article

Inflectional Description of Bulgarian Multiword Expressions: An Analysis of Two-Component Geographical Names

Svetlozara Leseva
1 links
Journal of Bulgarian Language
Linguistics, Language Diversity, and Identity
article

Inflectional Description of Bulgarian Multiword Expressions: An Analysis of Two-Component Geographical Names

Svetlozara Leseva
article en

Abstract

This article explores the grammatical and semantic characteristics of two-component compound proper names in Bulgarian, with a particular focus on geograph-ical names, aiming to define their inflectional types and integrate them into the respec-tive dictionary entries. The study is based on lexical units automatically extracted from internet sources, including structured resources such as Wikipedia. These units were manually filtered and categorised according to the type of referent, e.g. town, village, mountain, spring, etc. As a result, the initial dataset of 4,000 candidates was reduced to approximately 2,600 entries. The selected names were automatically POS-tagged and analysed in terms of their syntactic structure, lexical and grammatical categories, inflectional properties, defectivi-ty, etc. These features collectively determine the inflectional type of each named entity. The inflectional types enable both the generation of inflected forms and their recognition in free text.

Journal of Bulgarian LanguageVol. 72(PRIL)
Openalex Percentile: Top 93%
Linguistics, Language Diversity, and Identity
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