Assuming Facts Are Expressed More Than Once

Distant Supervision (DS) is a method for training sentence-level information extraction models using only an unlabeled corpus and a knowledge base (KB). Fundamental to many DS approaches is the assumption that KB facts are expressed at least once (EALO) in the text corpus. Often, however, KB facts are actually expressed in the corpus many times, in which cases EALO-based systems underuse the available training data. To address this problem, we introduce the "expressed at least alpha percent" (EALA) assumption, which asserts that expressions of KB facts account for up to alpha percent of the corresponding mentions. We show that for the same level of precision as the EALO approach, the EALA approach achieves up to 66 % higher recall on category recognition and 53 % higher recall on relation recognition.