Grammatical Bigrams

Unsupervised learning algorithms have been derived for several sta(cid: 173) tistical models of English grammar, but their computational com(cid: 173) plexity makes applying them to large data sets intractable. This paper presents a probabilistic model of English grammar that is much simpler than conventional models, but which admits an effi(cid: 173) cient EM training algorithm. The model is based upon grammat(cid: 173) ical bigrams, i. e. , syntactic relationships between pairs of words. We present the results of experiments that quantify the represen(cid: 173) tational adequacy of the grammatical bigram model, its ability to generalize from labelled data, and its ability to induce syntactic structure from large amounts of raw text.

Authors

• Mark Paskin

Keywords

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