Review of MALLET, produced by Andrew Kachites McCallum

Sat, 01 Dec 2012 00:00:00 +0000

MALLET Version: 2.0.7
Requirements: Java

Reviewed: 15 February 2013
Tested on: Mac OS X v. 10.8.2, and Windows 7

The MAchine Learning for LanguagE Toolkit, or MALLET, has been one of the “hottest” tools in digital humanities research. A product of the University of Massachusetts Amherst, written by Andrew McCallum and a team of collaborators, MALLET was originally released in 2002 but has received considerable renewed interest of late.¹ Fruitfully employing Latent Dirichlet Allocation [pdf], or LDA, MALLET can help navigate large bodies of information. It does so by finding clusters of words that frequently appear together, or “topics.” The algorithm imagines that any possible text or document within a corpus is a mixture of different topics; each topic is imagined to be a probability distribution of terms within and across that corpus. This leads to a variety of outputs, including lists of topics and their constituent words, and list of documents and their constituent topics. These results can often be astounding, and have even been (tongue-in-cheek) described as “magic.”

Imilligan on Journal of Digital Humanities

Review of MALLET, produced by Andrew Kachites McCallum