Lexical Attraction Models of Language

This paper presents lexical attraction models of language, in which the only explicitly represented linguistic knowledge is the likelihood of pairwise relations between words. This is in contrast with models that represent linguistic knowledge in terms of a lexicon, which assigns categories to each word, and a grammar, which expresses possible combinations in terms of these categories. The word-based nature and the simplicity of lexical attraction models make them good candidates for experiments in language learning. I introduce an unsupervised learning algorithm that uses lexical attraction and gives accuracy results comparable to supervised learning.