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Seed Word Selection for Weakly-Supervised Text Classification with Unsupervised Error Estimation
[article]
2021
arXiv
pre-print
Weakly-supervised text classification aims to induce text classifiers from only a few user-provided seed words. The vast majority of previous work assumes high-quality seed words are given. However, the expert-annotated seed words are sometimes non-trivial to come up with. Furthermore, in the weakly-supervised learning setting, we do not have any labeled document to measure the seed words' efficacy, making the seed word selection process "a walk in the dark". In this work, we remove the need
arXiv:2104.09765v1
fatcat:of3w45ct55bvvjzgh6rytrnqgu