Center for Automated Learning and Discovery
School of Computer Science, Carnegie Mellon University


Variational Inference and Learning for a Unified Model
of Syntax, Semantics and Morphology

Leonid Kontorovich, John Lafferty, David Blei*

April 2006

Keywords: Syntax, semantics, morphology, variational inference

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 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.

18 pages

*Computer Science Department, Princeton University, Princeton, NJ 08540.

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