Collecting Training Data using Anomaly Detection

An approach is provided in which an information handling system detects a multi-entity co-occurrence anomaly within a set of documents that corresponds to an amount of times that a first entity and a second entity co-occur in the set of documents. The information handling system then determines that at least one of the documents includes a title having a verb that grammatically connects the first entity to the second entity. As such, the information handling system collects document segments from the set of documents that have the first entity, the second entity, and the connecting verb. In turn, the information handling system uses the collected document segments to train a relation-based classifier.