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Weakly-supervised text classification aims to induce text classifiers fromonly a few user-provided seed words. The vast majority of previous work assumeshigh-quality seed words are given. However, the expert-annotated seed words aresometimes non-trivial to come up with. Furthermore, in the weakly-supervisedlearning setting, we do not have any labeled document to measure the seedwords' efficacy, making the seed word selection process "a walk in the dark".In this work, we remove the need for expert-curated seed words by first mining(noisy) candidate seed words associated with the category names. We then traininterim models with individual candidate seed words. Lastly, we estimate theinterim models' error rate in an unsupervised manner. The seed words that yieldthe lowest estimated error rates are added to the final seed word set. Acomprehensive evaluation of six binary classification tasks on four populardatasets demonstrates that the proposed method outperforms a baseline usingonly category name seed words and obtained comparable performance as acounterpart using expert-annotated seed words.

Seed Word Selection for Weakly-Supervised Text Classification with Unsupervised Error Estimation