In this paper we investigate the impact of morphological features on the task of automatically extending a dictionary. We approach the problem as a pattern classification task and compare the performance of several models in classifying nouns that are unknown to a broad coverage dictionary. We used a boosting classifier to compare the performance of models that use different sets of features. We show how adding simple morphological features to a model greatly improves the classification performance.
REF compared several models in classifying nouns with respect to a simplified version of WordNet and signified the gain in performance with morphological features.
Boosting Automatic Lexical Acquisition With Morphological Information
Workshop On Unsupervised Lexical Acquisition
Computer Science