Since the 1950s, many approaches have been proposed for assigning senses to words in context, although early attempts only served as models for toy systems. Currently, there are two main methodological approaches in this area: knowledge-based and corpus-based methods. Knowledge-based methods use external knowledge resources, which define explicit sense distinctions for assigning the correct sense of a word in context. Corpus-based methods use machine-learning techniques to induce models of word usages from large collections of text examples. Both knowledge-based and corpus-based methods present different benefits and drawbacks.