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        <title>Towards the Automatic Identification of Light Verb Constructions in Bulgarian</title>
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        <namePart type="given">Ivelina</namePart>
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    <abstract>This paper presents work in progress focused on developing a method for automatic identification of light verb constructions (LVCs) as a subclass of Bulgarian verbal MWEs. The method is based on machine learning and is trained on a set of LVCs extracted from the Bulgarian WordNet (BulNet) and the Bulgarian National Corpus (BulNC). The machine learning uses lexical, morphosyntactic, syntactic and semantic features of LVCs. We trained and tested two separate classifiers using the Java package Weka and two learning decision tree algorithms – J48 and RandomTree. The evaluation of the method includes 10-fold cross-validation on the training data from BulNet (F1 = 0.766 obtained by the J48 decision tree algorithm and F1 = 0.725 by the RandomTree algorithm), as well as evaluation of the performance on new instances from the BulNC (F1 = 0.802 by J48 and F1 = 0.607 by the RandomTree algorithm). Preliminary filtering of the candidates gives a slight improvement (F1 = 0.802 by J48 and F1 = 0.737 by RandomTree).</abstract>
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%0 Conference Proceedings
%T Towards the Automatic Identification of Light Verb Constructions in Bulgarian
%A Stoyanova, Ivelina
%A Leseva, Svetlozara
%A Todorova, Maria
%S Proceedings of the Second International Conference on Computational Linguistics in Bulgaria (CLIB 2016)
%D 2016
%8 September
%I Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences
%C Sofia, Bulgaria
%F stoyanova-etal-2016-towards
%X This paper presents work in progress focused on developing a method for automatic identification of light verb constructions (LVCs) as a subclass of Bulgarian verbal MWEs. The method is based on machine learning and is trained on a set of LVCs extracted from the Bulgarian WordNet (BulNet) and the Bulgarian National Corpus (BulNC). The machine learning uses lexical, morphosyntactic, syntactic and semantic features of LVCs. We trained and tested two separate classifiers using the Java package Weka and two learning decision tree algorithms – J48 and RandomTree. The evaluation of the method includes 10-fold cross-validation on the training data from BulNet (F1 = 0.766 obtained by the J48 decision tree algorithm and F1 = 0.725 by the RandomTree algorithm), as well as evaluation of the performance on new instances from the BulNC (F1 = 0.802 by J48 and F1 = 0.607 by the RandomTree algorithm). Preliminary filtering of the candidates gives a slight improvement (F1 = 0.802 by J48 and F1 = 0.737 by RandomTree).
%U https://aclanthology.org/2016.clib-1.4
%P 28-37

Markdown (Informal)

[Towards the Automatic Identification of Light Verb Constructions in Bulgarian](https://aclanthology.org/2016.clib-1.4) (Stoyanova et al., CLIB 2016)

• Towards the Automatic Identification of Light Verb Constructions in Bulgarian (Stoyanova et al., CLIB 2016)

ACL

• Ivelina Stoyanova, Svetlozara Leseva, and Maria Todorova. 2016. Towards the Automatic Identification of Light Verb Constructions in Bulgarian. In Proceedings of the Second International Conference on Computational Linguistics in Bulgaria (CLIB 2016), pages 28–37, Sofia, Bulgaria. Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences.