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Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training

12 Jun 2023arXiv:2306.07193archive 2025-07-28

ran Xu, Yue Yu, Joyce C. Ho, Carl Yang

Scientific document classification is a critical task for a wide range of applications, but the cost of obtaining massive amounts of human-labeled data can be prohibitive. To address this challenge, we propose a weakly-supervised approach for scientific document classification using label names only. In scientific domains, label names often include domain-specific concepts that may not appear in the document corpus, making it difficult to match labels and documents precisely. To tackle this issue, we propose WANDER, which leverages dense retrieval to perform matching in the embedding space to capture the semantics of label names. We further design the label name expansion module to enrich the label name representations. Lastly, a self-training step is used to refine the predictions. The experiments on three datasets show that WANDER outperforms the best baseline by 11.9% on average. Our code will be published at https://github.com/ritaranx/wander.

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load_pred_data ritaranx/wander/dense_retrieval.py official repository ran · our draft was wrong MIT (permissive) · 9dc9d7b5719a1ecc · report
tensor_to_numpy ritaranx/wander/keyword_expansion.py official repository ran · honoured contract MIT (permissive) · 0450e58f35f90bdf · report

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Document ClassificationRetrieval

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