Too Many Mammals: Improving the Diversity of Automatically Recognized Terms

Automatic Term Recognition systems extract domain-specific terms from text corpora. Un-fortunately current systems fail to capture the whole of the domain covered by a corpus. To address this problem, we present a novel term re-ranking method that generates term lists con-taining terms that are not only individually salient, but also contribute to a globally diverse list that is truly representative of the corpus. We show that, even without any prior knowl-edge about the domain, our proposed method improves the diversity of the results produced by two popular automatic term recognition algo-rithms