Unsupervised Learning of Generalized Names

We present an algorithm, Nomen, for learning generalized names in text. Examples of these are names of diseases and infectious agents, such as bacteria and viruses. These names exhibit certain properties that make their identification more complex than that of regular proper names. Nomen uses a novel form of bootstrapping to grow sets of textual instances and of their contextual patterns. The algorithm makes use of competing evidence to boost the learning of several categories of names simultaneously. We present results of the algorithm on a large corpus. We also investigate the relative merits of several evaluation strategies.

• Type: Journal
• Year: 2002
• Where: COLING
• Authors: Roman Yangarber, Winston Lin, Ralph Grishman
• Keywords: Algorithm, COLING 2002, COLING 2008, Infectious Agents, Regular Proper Names