Named-Entity Recognition in Novel Domains with External Lexical Knowledge
We investigate the adaptation of structured classifiers to new domains. In particular, the problem of using a supervised Named-Entity Recognition (NER) system on data from a different source than the training data. We present a Semi-Markov Model, trained with the perceptron algorithm, coupled with an external dictionary with the goal of improving generalization on the novel domain. Preliminary experiments show promising results, obtained with very simple additional features.