SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble

Shen, Jiaming; Wu, Zeqiu; Lei, Dongming; Shang, Jingbo; Ren, Xiang

doi:10.48550/arxiv.1910.08192

Cited by 1 publication

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Distributional methods predict hypernymy relations by calculating pairwise scores based on term embeddings [5,10,14,22,26]. Additionally, some methods employ advanced techniques, such as transfer learning [23], reinforcement learning [17], and entity set expansion techniques [25], to construct a taxonomy.…”

Section: Related Workmentioning

confidence: 99%

Towards Visual Taxonomy Expansion

Zhu,

Liu,

Liang

et al. 2023

Proceedings of the 31st ACM International Conference on Multimedia

View full text Add to dashboard Cite

Taxonomy expansion task is essential in organizing the ever-increasing volume of new concepts into existing taxonomies. Most existing methods focus exclusively on using textual semantics, leading to an inability to generalize to unseen terms and the "Prototypical Hypernym Problem." In this paper, we propose Visual Taxonomy Expansion (VTE), introducing visual features into the taxonomy expansion task. We propose a textual hypernymy learning task and a visual prototype learning task to cluster textual and visual semantics. In addition to the tasks on respective modalities, we introduce a hyper-proto constraint that integrates textual and visual semantics to produce fine-grained visual semantics. Our method is evaluated on two datasets, where we obtain compelling results. Specifically, on the Chinese taxonomy dataset, our method significantly improves accuracy by 8.75 %. Additionally, our approach performs better than ChatGPT on the Chinese taxonomy dataset. CCS CONCEPTS• Computing methodologies → Information extraction.

show abstract