SenseClusters / News: Recent posts

SenseClusters version 1.01 released

We are pleased to announce the release of version 1.01 of the SenseClusters package. This is a major upgrade from our last stable release (0.95) so you are encouraged to move to 1.01 at the earliest opportunity. Quite a few changes have been made to the documentation and installation procedures, and there have been significant changes in how Singular Value Decomposition is handled. Please see http://senseclusters.sourceforge.net for additional details.

Posted by Ted Pedersen 2008-04-06

SenseClusters v0.95 released (adds Latent Semantic Analysis)

We are pleased to announce the release of SenseClusters version 0.95.

SenseClusters is a freely available package that allows you to cluster similar contexts, or to identify clusters of related words. It is fully
unsupervised, and can automatically discover the optimal number of clusters in your text.

As of version 0.95, we now fully support Latent Semantic Analysis for context and word clustering, and we continue to improve the native
SenseClusters methods, which include the ability to cluster first and second order representations of context.... read more

Posted by Ted Pedersen 2006-08-26

SenseClusters version 0.93 released!

We are very pleased to announce the release of SenseClusters
version 0.93. This version marks our first steps towards supporting
Latent Semantic Analysis in addition to our native SenseClusters
methods.

In this version we now support word clustering (feature clustering
really, as it is not limited to just unigrams or single words) that
is based on a feature by context representation. In other words,
features are clustered based on the contexts in which they occur.
These matrices can optionally be reduced with SVD prior to clustering.
We refer to this as LSA feature clustering.... read more

Posted by Ted Pedersen 2006-07-09

Discovering Word Meanings with SenseClusters!

We are pleased to announce the release of SenseClusters, a free software package that does unsupervised clustering or discovery of word senses in raw text. SenseClusters can be used to carry out clustering in either vector and similarity spaces via a range of agglomerative and partional
algorithms. It supports everything from average link clustering to much more complicated procedures such as Latent Semantic Analysis (LSA).... read more

Posted by Ted Pedersen 2004-01-04