Paper: A Self-Learning Universal Concept Spotter

ACL ID C96-2157
Title A Self-Learning Universal Concept Spotter
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1996
Authors

We describe the Universal Spotter, a system for identifying in-text references to entities of an arbitrary, user-sl)ecitied type, such its people, organizations, equipment, products, materials, etc. Starting with some initial seed examples, and a training text eortms, I;he system generates rules that will find fllrther con- cepts of the stone type. The initial se, ed information is t)rovided by the user in the form of a typical lexical context in which the enl, ities to be spotted occur, e.g., "the name ends with Co.", or %o the right of produced or made", and so forth, or by simt)ly supplying examples of the concept itself, e.g., Ford Tau'r'as, gas turbine, Bi 9 Mac. In addition, nega- tive exalnples can t)e supplied, if known. Given a suf[ieiently large training corpus, an unsupervise(t ...