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    <abstract>As an essential form of knowledge representation, taxonomies are widely used in various downstream natural language processing tasks. However, with the continuously rising of new concepts, many existing taxonomies are unable to maintain coverage by manual expansion. In this paper, we propose TEMP, a self-supervised taxonomy expansion method, which predicts the position of new concepts by ranking the generated taxonomy-paths. For the first time, TEMP employs pre-trained contextual encoders in taxonomy construction and hypernym detection problems. Experiments prove that pre-trained contextual embeddings are able to capture hypernym-hyponym relations. To learn more detailed differences between taxonomy-paths, we train the model with dynamic margin loss by a novel dynamic margin function. Extensive evaluations exhibit that TEMP outperforms prior state-of-the-art taxonomy expansion approaches by 14.3% in accuracy and 15.8% in mean reciprocal rank on three public benchmarks.</abstract>
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%0 Conference Proceedings
%T TEMP: Taxonomy Expansion with Dynamic Margin Loss through Taxonomy-Paths
%A Liu, Zichen
%A Xu, Hongyuan
%A Wen, Yanlong
%A Jiang, Ning
%A Wu, HaiYing
%A Yuan, Xiaojie
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F liu-etal-2021-temp
%X As an essential form of knowledge representation, taxonomies are widely used in various downstream natural language processing tasks. However, with the continuously rising of new concepts, many existing taxonomies are unable to maintain coverage by manual expansion. In this paper, we propose TEMP, a self-supervised taxonomy expansion method, which predicts the position of new concepts by ranking the generated taxonomy-paths. For the first time, TEMP employs pre-trained contextual encoders in taxonomy construction and hypernym detection problems. Experiments prove that pre-trained contextual embeddings are able to capture hypernym-hyponym relations. To learn more detailed differences between taxonomy-paths, we train the model with dynamic margin loss by a novel dynamic margin function. Extensive evaluations exhibit that TEMP outperforms prior state-of-the-art taxonomy expansion approaches by 14.3% in accuracy and 15.8% in mean reciprocal rank on three public benchmarks.
%R 10.18653/v1/2021.emnlp-main.313
%U https://aclanthology.org/2021.emnlp-main.313
%U https://doi.org/10.18653/v1/2021.emnlp-main.313
%P 3854-3863

Markdown (Informal)

[TEMP: Taxonomy Expansion with Dynamic Margin Loss through Taxonomy-Paths](https://aclanthology.org/2021.emnlp-main.313) (Liu et al., EMNLP 2021)

• TEMP: Taxonomy Expansion with Dynamic Margin Loss through Taxonomy-Paths (Liu et al., EMNLP 2021)

ACL

• Zichen Liu, Hongyuan Xu, Yanlong Wen, Ning Jiang, HaiYing Wu, and Xiaojie Yuan. 2021. TEMP: Taxonomy Expansion with Dynamic Margin Loss through Taxonomy-Paths. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3854–3863, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.