Document Set Expansion with Positive-Unlabeled Learning Using Intractable Density Estimation

Haiyang Zhang, Qiuyi Chen, Yanjie Zou, Jia Wang, Yushan Pan, Mark Stevenson

Abstract
The Document Set Expansion (DSE) task involves identifying relevant documents from large collections based on a limited set of example documents. Previous research has highlighted Positive and Unlabeled (PU) learning as a promising approach for this task. However, most PU methods rely on the unrealistic assumption of knowing the class prior for positive samples in the collection. To address this limitation, this paper introduces a novel PU learning framework that utilizes intractable density estimation models. Experiments conducted on PubMed and Covid datasets in a transductive setting showcase the effectiveness of the proposed method for DSE. Code is available from https://github.com/Beautifuldog01/Document-set-expansion-puDE.
Anthology ID:
2024.lrec-main.460
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
5167–5173
Language:
URL:
https://aclanthology.org/2024.lrec-main.460
DOI:
Bibkey:
Cite (ACL):
Haiyang Zhang, Qiuyi Chen, Yanjie Zou, Jia Wang, Yushan Pan, and Mark Stevenson. 2024. Document Set Expansion with Positive-Unlabeled Learning Using Intractable Density Estimation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 5167–5173, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Document Set Expansion with Positive-Unlabeled Learning Using Intractable Density Estimation (Zhang et al., LREC-COLING 2024)
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https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.460.pdf
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