Related papers: Find Parent then Label Children: A Two-stage Taxonomy Completion Method with Pre-trained Language Model

Find Parent then Label Children: A Two-stage Taxonomy Completion Method with Pre-trained Language Model

URL: http://arxiv.org/abs/2406.17739v1
Date: Tue, 25 Jun 2024 17:25:02 GMT
Title: Find Parent then Label Children: A Two-stage Taxonomy Completion Method with Pre-trained Language Model
Authors: Fei Xia, Yixuan Weng, Shizhu He, Kang Liu, Jun Zhao,
Abstract summary: We propose a two-stage method called ATTEMPT for taxonomy completion. Our method inserts new concepts into the correct position by finding a parent node and labeling child nodes. We take advantage of pre-trained language models for hypernym/hyponymy recognition.
Score: 46.00652942385366
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Taxonomies, which organize domain concepts into hierarchical structures, are crucial for building knowledge systems and downstream applications. As domain knowledge evolves, taxonomies need to be continuously updated to include new concepts. Previous approaches have mainly focused on adding concepts to the leaf nodes of the existing hierarchical tree, which does not fully utilize the taxonomy's knowledge and is unable to update the original taxonomy structure (usually involving non-leaf nodes). In this paper, we propose a two-stage method called ATTEMPT for taxonomy completion. Our method inserts new concepts into the correct position by finding a parent node and labeling child nodes. Specifically, by combining local nodes with prompts to generate natural sentences, we take advantage of pre-trained language models for hypernym/hyponymy recognition. Experimental results on two public datasets (including six domains) show that ATTEMPT performs best on both taxonomy completion and extension tasks, surpassing existing methods.

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