Language independent, minimally supervised induction of lexical probabilities
Silviu Cucerzan, David Yarowsky · 2000
A central problem in part-of-speech tagging, especially for new languages for which limited annotated resources are available, is estimating the distribution of lexical probabilities for unknown words. This paper introduces a new paradigmatic similarity measure and presents a minimally supervised learning approach combining effective selection and weighting methods based on paradigmatic and contextual similarity measures populated from large quantities of inexpensive raw text data. This approach is highly language independent and requires no modification to the algorithm or implementation to shift between languages such as French and English.