,We discuss sequential tagging problems in natural language processing using statistical methodology. We propose an automatic and domain-independent approach to modeling out-ofvocabulary (OOV) words, that is words that do not occur in training data. Our method is based on using probabilistic letter n-gram models to model orthography of different tags. We show how to combine the approach with two widely used statistical models Hidden Markov Models and Conditional Random Fields. Instead of taking the common approach of directly using sub-strings as features resulting in an explosion in the number of model parameters, we compress orthographic information into a small number of parameters. Experiments in biomedical entity recognition on the Genia corpus show that the approach can alleviate the OOV problem resulting in improvement in overall model performance.
['Teemu Ruokolainen', 'Miikka Silfverberg']
Modeling OOV Words With Letter N-Grams in Statistical Taggers: Preliminary Work in Biomedical Entity Recognition