Tagging unknown proper names using decision trees

Abstract

This paper describes a supervised learning method to automatically select from a set of noun phrases, embedding proper names of different semantic classes, their most distinctive features. The result of the learning process is a decision tree which classifies an unknown proper name on the basis of its context of occurrence. This classifier is used to estimate the probability distribution of an out of vocabulary proper name over a tagset. This probability distribution is itself used to estimate the parameters of a stochastic part of speech tagger.