Linguistic Geometries for Unsupervised Dimensionality Reduction

Details Linguistic Geometries for Unsupervised Dimensionality Reduction Linguistic Geometries for Unsupervised Dimensionality Reduction

2010-03-02

arxiv.org/abs/1003.0628v1

Computer Science

Computation and Language

13 pages, 15 figures

Scientific paper

Text documents are complex high dimensional objects. To effectively visualize such data it is important to reduce its dimensionality and visualize the low dimensional embedding as a 2-D or 3-D scatter plot. In this paper we explore dimensionality reduction methods that draw upon domain knowledge in order to achieve a better low dimensional embedding and visualization of documents. We consider the use of geometries specified manually by an expert, geometries derived automatically from corpus statistics, and geometries computed from linguistic resources.

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