TEAM: A multitask learning based Taxonomy Expansion approach for Attach and Merge

Abstract

Taxonomy expansion is a crucial task. Most of the taxonomy expansion approaches are of two types, attach and merge. In a taxonomy like WordNet, both merge and attach are integral parts of the expansion operations, but the majority of studies consider them separately. This paper proposes a novel multi-task learning-based deep learning method known as Taxonomy Expansion with Attach and Merge (TEAM) that performs both the merge and attach operations. This is the first study that integrates both the merge and attach operations in a single model to the best of our knowledge. The proposed models have been evaluated on three separate WordNet taxonomies, viz., Assamese, Bangla, and Hindi. From the various experimental setups, it is shown that TEAM outperforms its state-of-the-art counterparts for attach operation and also provides highly encouraging performance for the merge operation.

TacoPrompt: A Collaborative Multi-Task Prompt Learning Method for Self-Supervised Taxonomy Completion

“…Based on the assumption of the problem, the studies can be divided into two main categories: taxonomy expansion and completion. In taxonomy expansion, researchers Yu et al, 2020;Ma et al, 2021;Wang et al, 2021a;Liu et al, 2021;Takeoka et al, 2021;Zhai et al, 2023;Jiang et al, 2023;Cheng et al, 2022;Phukon et al, 2022) suggest that new concepts can only be added as leaf nodes. However, this assumption is inappropriate for real-world applications: the query nodes can also be added as non-leaf nodes to the taxonomy (Zhang et al, 2021).…”