Carnegie Mellon University
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Variational inference and learning for a unified model of syntax, semantics and morphology

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journal contribution
posted on 2004-07-01, 00:00 authored by Leonid Kontorovich, John Lafferty, David Blei
Abstract: "There have been recent attempts to produce trainable (unsupervised) models of human-language syntax and semantics, as well as morphology. To our knowledge, there has not been an attempt to produce a generative model that encorporates [sic] semantic, syntactic, and morphological elements. Some immediate applications of this tool are stemming, work clustering by root, and disambiguation (at the syntactic, semantic, and morphological levels). In this work, we propose a hierarchical topics-syntax-morphology model. We provide the variational inference and update rules for this model (exact inference is intractable). We show some preliminary results on segmentation tasks."