Tagging Unknown Words with Raw Text Features
David Vadas, James Curran · 2005
Processing unknown words is disproportionately important because of their high information content. It is crucial in domains with specialist vocabularies where relevant training material is scarce, for example: biological text. Unknown word processing often begins with Part of Speech (POS) tagging, where accuracy is typically 10% worse than on known words. We demonstrate that features extracted from large raw text corpora can significantly increase accuracy on unknown words. These features supply a large part of what we are missing with unknown words: context information about how the word is used. We describe a Maximum Entropy modelling approach which uses real-valued features to represent unannotated contextual information. Our initial experiments with real-valued features have resulted in an increased accuracy from 87.39% to 88.85% on unknown words.