@inproceedings{koeva-etal-2016-automatic,
title = "Automatic Prediction of Morphosemantic Relations",
author = "Koeva, Svetla and
Leseva, Svetlozara and
Stoyanova, Ivelina and
Dimitrova, Tsvetana and
Todorova, Maria",
editor = "Fellbaum, Christiane and
Vossen, Piek and
Mititelu, Verginica Barbu and
Forascu, Corina",
booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
month = "27--30 " # jan,
year = "2016",
address = "Bucharest, Romania",
publisher = "Global Wordnet Association",
url = "https://aclanthology.org/2016.gwc-1.26",
pages = "169--177",
abstract = "This paper presents a machine learning method for automatic identification and classification of morphosemantic relations (MSRs) between verb and noun synset pairs in the Bulgarian WordNet (BulNet). The core training data comprise 6,641 morphosemantically related verb{--}noun literal pairs from BulNet. The core dataset were preprocessed quality-wise by applying validation and reorganisation procedures. Further, the data were supplemented with negative examples of literal pairs not linked by an MSR. The designed supervised machine learning method uses the RandomTree algorithm and is implemented in Java with the Weka package. A set of experiments were performed to test various approaches to the task. Future work on improving the classifier includes adding more training data, employing more features, and fine-tuning. Apart from the language specific information about derivational processes, the proposed method is language independent.",
}
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<abstract>This paper presents a machine learning method for automatic identification and classification of morphosemantic relations (MSRs) between verb and noun synset pairs in the Bulgarian WordNet (BulNet). The core training data comprise 6,641 morphosemantically related verb–noun literal pairs from BulNet. The core dataset were preprocessed quality-wise by applying validation and reorganisation procedures. Further, the data were supplemented with negative examples of literal pairs not linked by an MSR. The designed supervised machine learning method uses the RandomTree algorithm and is implemented in Java with the Weka package. A set of experiments were performed to test various approaches to the task. Future work on improving the classifier includes adding more training data, employing more features, and fine-tuning. Apart from the language specific information about derivational processes, the proposed method is language independent.</abstract>
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%0 Conference Proceedings
%T Automatic Prediction of Morphosemantic Relations
%A Koeva, Svetla
%A Leseva, Svetlozara
%A Stoyanova, Ivelina
%A Dimitrova, Tsvetana
%A Todorova, Maria
%Y Fellbaum, Christiane
%Y Vossen, Piek
%Y Mititelu, Verginica Barbu
%Y Forascu, Corina
%S Proceedings of the 8th Global WordNet Conference (GWC)
%D 2016
%8 27–30 jan
%I Global Wordnet Association
%C Bucharest, Romania
%F koeva-etal-2016-automatic
%X This paper presents a machine learning method for automatic identification and classification of morphosemantic relations (MSRs) between verb and noun synset pairs in the Bulgarian WordNet (BulNet). The core training data comprise 6,641 morphosemantically related verb–noun literal pairs from BulNet. The core dataset were preprocessed quality-wise by applying validation and reorganisation procedures. Further, the data were supplemented with negative examples of literal pairs not linked by an MSR. The designed supervised machine learning method uses the RandomTree algorithm and is implemented in Java with the Weka package. A set of experiments were performed to test various approaches to the task. Future work on improving the classifier includes adding more training data, employing more features, and fine-tuning. Apart from the language specific information about derivational processes, the proposed method is language independent.
%U https://aclanthology.org/2016.gwc-1.26
%P 169-177
Markdown (Informal)
[Automatic Prediction of Morphosemantic Relations](https://aclanthology.org/2016.gwc-1.26) (Koeva et al., GWC 2016)
ACL
- Svetla Koeva, Svetlozara Leseva, Ivelina Stoyanova, Tsvetana Dimitrova, and Maria Todorova. 2016. Automatic Prediction of Morphosemantic Relations. In Proceedings of the 8th Global WordNet Conference (GWC), pages 169–177, Bucharest, Romania. Global Wordnet Association.