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    <abstract>In this paper we present an approach to support production of synsets for Serbian WordNet (SerWN) by adjusting Princeton WordNet (PWN) synsets using several bilingual English-Serbian resources. PWN synset definitions were automatically translated and post-edited, if needed, while candidate literals for Serbian synsets were obtained automatically from a list of translational equivalents compiled form bilingual resources. Preliminary results obtained from a set of 1248 selected PWN synsets show that the produced Serbian synsets contain 4024 literals, out of which 2278 were offered by the system we present in this paper, whereas experts added the remaining 1746. Approximately one half of synset definitions obtained automatically were accepted with no or minor corrections. These first results are encouraging, since the efficiency of synset production for SerWN was increased. There is also space for further improvement of this approach to wordnet enrichment.</abstract>
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%0 Conference Proceedings
%T Resource-based WordNet Augmentation and Enrichment
%A Stanković, Ranka
%A Mladenović, Miljana
%A Obradović, Ivan
%A Vitas, Marko
%A Krstev, Cvetana
%S Proceedings of the Third International Conference on Computational Linguistics in Bulgaria (CLIB 2018)
%D 2018
%8 May
%I Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences
%C Sofia, Bulgaria
%F stankovic-etal-2018-resource
%X In this paper we present an approach to support production of synsets for Serbian WordNet (SerWN) by adjusting Princeton WordNet (PWN) synsets using several bilingual English-Serbian resources. PWN synset definitions were automatically translated and post-edited, if needed, while candidate literals for Serbian synsets were obtained automatically from a list of translational equivalents compiled form bilingual resources. Preliminary results obtained from a set of 1248 selected PWN synsets show that the produced Serbian synsets contain 4024 literals, out of which 2278 were offered by the system we present in this paper, whereas experts added the remaining 1746. Approximately one half of synset definitions obtained automatically were accepted with no or minor corrections. These first results are encouraging, since the efficiency of synset production for SerWN was increased. There is also space for further improvement of this approach to wordnet enrichment.
%U https://aclanthology.org/2018.clib-1.14/
%P 104-114

Markdown (Informal)

[Resource-based WordNet Augmentation and Enrichment](https://aclanthology.org/2018.clib-1.14/) (Stanković et al., CLIB 2018)

• Resource-based WordNet Augmentation and Enrichment (Stanković et al., CLIB 2018)

ACL

• Ranka Stanković, Miljana Mladenović, Ivan Obradović, Marko Vitas, and Cvetana Krstev. 2018. Resource-based WordNet Augmentation and Enrichment. In Proceedings of the Third International Conference on Computational Linguistics in Bulgaria (CLIB 2018), pages 104–114, Sofia, Bulgaria. Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences.