  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 for expert-curated seed words by first mining
(noisy) candidate seed words associated with the category names. We then train
interim models with individual candidate seed words. Lastly, we estimate the
interim models' error rate in an unsupervised manner. The seed words that yield
the lowest estimated error rates are added to the final seed word set. A
comprehensive evaluation of six binary classification tasks on four popular
datasets demonstrates that the proposed method outperforms a baseline using
only category name seed words and obtained comparable performance as a
counterpart using expert-annotated seed words.
