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 this https URL

本研究提出了一种弱监督方法 (WANDER) 用于科学文献分类，使用标签名称作为唯一标注，利用稠密检索匹配方式捕捉标签名称的语义，并设计标签名称扩展模块来丰富标签名称的表示，最后使用自我训练步骤来细化预测，该方法在三个数据集上的表现优于最佳基准线约 11.9％。