Active Learning for Finding a Needle in a Haystack
Training a classifier to find a rare class of documents, such as hate speech directed at a particular religious group, requires labelled examples that are expensive to obtain when the class makes up only a small fraction of a corpus. Active learning reduces this cost, but it must start from an initial labelled set that contains examples of the target class. Random sampling cannot supply these at reasonable cost, so they must be selected in some other way, such as keyword search, and any such selection biases the initial set towards part of the target class. We evaluate whether active learning can correct this bias. On AG News and Measuring Hate Speech, at a prevalence of around 1%, we com...
| Authors | Emily Silcock, Simon Löwe |
|---|---|
| Primary category | cs.CL |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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