Computer Science Colloquium, 2005-2006

Zach Solan, School of Physics and Astronomy, Tel-Aviv University
January 25th, 2006

Unsupervised learning of natural languages

We address the problem, fundamental to linguistics, bioinformatics and certain other disciplines, of using corpora of raw symbolic sequential data to infer underlying rules that govern their production. Given a corpus of strings (such as text, transcribed speech, nucleotide base pairs, amino acid sequence data, musical notation, etc.), our unsupervised algorithm recursively distills from it hierarchically structured patterns. The ADIOS (Automatic Distillation of Structure) algorithm relies on a statistical method for pattern extraction and on structured generalization, two processes that have been implicated in language acquisition. It has been evaluated on artificial context-free grammars with thousands of rules, on natural languages as diverse as English and Chinese, on coding regions in DNA sequences, and on protein data correlating sequence with function. This is the first time an unsupervised algorithm is shown capable of learning complex syntax, generating grammatical novel sentences, scoring well in standard language proficiency tests, and proving useful in other fields that call for structure discovery from raw data, such as bioinformatics.

Shuly Wintner
Last modified: Thu Jan 19 17:03:59 IST 2006