Light Verb Constructions in ELEXIS-WSD – Annotation, Comparisons and Issues

This paper deals with light verb constructions and their annotation in ELEXIS-sr, the Serbian extension of the ELEXIS-WSD corpus. In Section 1, general introductory remarks are given about these constructions, the notion of light verbs, and their treatment and further classification in the PARSEME annotation guidelines (subtypes LVC.full and LVC.cause). Section 2 offers an insight into ELEXIS-WSD corpus, annotated with VMWEs for several languages, with a remark that these VMWEs were not further subcategorised into finer classes. For this paper, we classified them ourselves to facilitate comparisons of the LVCs annotated in ELEXIS-sr. Tools and resources used for the automatic annotation of ELEXIS-sr are presented in Section 3, as well as the results of manual checking. In Section 4, we offer a comparison of LVCs in four ELEXIS-WSD sub-collections: Serbian, Bulgarian, Slovene, and English. We use Serbian as a starting point for this comparison, as it has been thoroughly annotated with MWEs (and NEs). We present the results of the comparison of all the occurrences of LVCs in the Serbian extension with their occurrences and annotation both in ELEXIS-WSD and Parseme sub-corpora for other languages. An important conclusion is that the most equivalents among LVCs are between Serbian and Bulgarian, closely related Slavic languages (a total of 34 equivalents), while between Serbian and Slovene, also Slavic, there are 11 equivalents, as between Serbian and English. It seems that this could be explained by the number of VMWES and LVCs annotated, or by the strategy used by different annotators.

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Journal
Computational Linguistics in Bulgaria
Published
2025-01-01
DOI
https://doi.org/10.47810/jclib.1.2025.03
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Light Verb Constructions in ELEXIS-WSD – Annotation, Comparisons and Issues

Ranka Stanković, Cvetana Krstev, Aleksandra Marković
Computational Linguistics in Bulgaria
Natural Language Processing Techniques
article

Light Verb Constructions in ELEXIS-WSD – Annotation, Comparisons and Issues

Ranka Stanković, Cvetana Krstev, Aleksandra Marković
article en

Abstract

This paper deals with light verb constructions and their annotation in ELEXIS-sr, the Serbian extension of the ELEXIS-WSD corpus. In Section 1, general introductory remarks are given about these constructions, the notion of light verbs, and their treatment and further classification in the PARSEME annotation guidelines (subtypes LVC.full and LVC.cause). Section 2 offers an insight into ELEXIS-WSD corpus, annotated with VMWEs for several languages, with a remark that these VMWEs were not further subcategorised into finer classes. For this paper, we classified them ourselves to facilitate comparisons of the LVCs annotated in ELEXIS-sr. Tools and resources used for the automatic annotation of ELEXIS-sr are presented in Section 3, as well as the results of manual checking. In Section 4, we offer a comparison of LVCs in four ELEXIS-WSD sub-collections: Serbian, Bulgarian, Slovene, and English. We use Serbian as a starting point for this comparison, as it has been thoroughly annotated with MWEs (and NEs). We present the results of the comparison of all the occurrences of LVCs in the Serbian extension with their occurrences and annotation both in ELEXIS-WSD and Parseme sub-corpora for other languages. An important conclusion is that the most equivalents among LVCs are between Serbian and Bulgarian, closely related Slavic languages (a total of 34 equivalents), while between Serbian and Slovene, also Slavic, there are 11 equivalents, as between Serbian and English. It seems that this could be explained by the number of VMWES and LVCs annotated, or by the strategy used by different annotators.

Computational Linguistics in BulgariaVol. 1(1)
Center for Health, Exercise and Sport Sciences (RS)
Science Fund of the Republic of Serbia
Openalex Percentile: Top 87%
Natural Language Processing Techniques
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