@inproceedings{nikishina-etal-2022-taxfree,
title = "{T}ax{F}ree: a Visualization Tool for Candidate-free Taxonomy Enrichment",
author = "Nikishina, Irina and
Andrianov, Ivan and
Vakhitova, Alsu and
Panchenko, Alexander",
editor = "Buntine, Wray and
Liakata, Maria",
booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations",
month = nov,
year = "2022",
address = "Taipei, Taiwan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-demo.5",
pages = "39--47",
abstract = "Taxonomies are widely used in a various number of downstream NLP tasks and, therefore, should be kept up-to-date. In this paper, we present TaxFree, an open source system for taxonomy visualisation and automatic Taxonomy Enrichment without pre-defined candidates on the example of WordNet-3.0. As oppose to the traditional task formulation (where the list of new words is provided beforehand), we provide an approach for automatic extension of a taxonomy using a large pre-trained language model. As an advantage to the existing visualisation tools of WordNet, TaxFree also integrates graphic representations of synsets from ImageNet. Such visualisation tool can be used for both updating taxonomies and inspecting them for the required modifications.",
}
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<abstract>Taxonomies are widely used in a various number of downstream NLP tasks and, therefore, should be kept up-to-date. In this paper, we present TaxFree, an open source system for taxonomy visualisation and automatic Taxonomy Enrichment without pre-defined candidates on the example of WordNet-3.0. As oppose to the traditional task formulation (where the list of new words is provided beforehand), we provide an approach for automatic extension of a taxonomy using a large pre-trained language model. As an advantage to the existing visualisation tools of WordNet, TaxFree also integrates graphic representations of synsets from ImageNet. Such visualisation tool can be used for both updating taxonomies and inspecting them for the required modifications.</abstract>
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%0 Conference Proceedings
%T TaxFree: a Visualization Tool for Candidate-free Taxonomy Enrichment
%A Nikishina, Irina
%A Andrianov, Ivan
%A Vakhitova, Alsu
%A Panchenko, Alexander
%Y Buntine, Wray
%Y Liakata, Maria
%S Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations
%D 2022
%8 November
%I Association for Computational Linguistics
%C Taipei, Taiwan
%F nikishina-etal-2022-taxfree
%X Taxonomies are widely used in a various number of downstream NLP tasks and, therefore, should be kept up-to-date. In this paper, we present TaxFree, an open source system for taxonomy visualisation and automatic Taxonomy Enrichment without pre-defined candidates on the example of WordNet-3.0. As oppose to the traditional task formulation (where the list of new words is provided beforehand), we provide an approach for automatic extension of a taxonomy using a large pre-trained language model. As an advantage to the existing visualisation tools of WordNet, TaxFree also integrates graphic representations of synsets from ImageNet. Such visualisation tool can be used for both updating taxonomies and inspecting them for the required modifications.
%U https://aclanthology.org/2022.aacl-demo.5
%P 39-47
Markdown (Informal)
[TaxFree: a Visualization Tool for Candidate-free Taxonomy Enrichment](https://aclanthology.org/2022.aacl-demo.5) (Nikishina et al., AACL-IJCNLP 2022)
ACL
- Irina Nikishina, Ivan Andrianov, Alsu Vakhitova, and Alexander Panchenko. 2022. TaxFree: a Visualization Tool for Candidate-free Taxonomy Enrichment. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations, pages 39–47, Taipei, Taiwan. Association for Computational Linguistics.