  The California Innocence Project (CIP), a clinical law school program aiming
to free wrongfully convicted prisoners, evaluates thousands of mails containing
new requests for assistance and corresponding case files. Processing and
interpreting this large amount of information presents a significant challenge
for CIP officials, which can be successfully aided by topic modeling
techniques.In this paper, we apply Non-negative Matrix Factorization (NMF)
method and implement various offshoots of it to the important and previously
unstudied data set compiled by CIP. We identify underlying topics of existing
case files and classify request files by crime type and case status (decision
type). The results uncover the semantic structure of current case files and can
provide CIP officials with a general understanding of newly received case files
before further examinations. We also provide an exposition of popular variants
of NMF with their experimental results and discuss the benefits and drawbacks
of each variant through the real-world application.
