Exploiting role-identifying nouns and expressions for information extraction

We present a new approach for extraction pattern learning that exploits role-identifying nouns, which are nouns whose semantics reveal the role that they play in an event (e.g., an "assassin" is a perpetrator). Given a few seed nouns, a bootstrapping algorithm automatically learns role-identifying nouns, which are then used to learn extraction patterns. We also introduce a method to learn role-identifying expressions, which consist of a role-identifying verb linked to an event (e.g., "<subject> participated in the murder"). We present experimental results on the MUC-4 terrorism corpus and a disease outbreaks corpus.