Analysis of Legal Documents via Non-negative Matrix Factorization Methods

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 o cials, which can be successfully aided by topic modeling techniques.In this paper, we apply Non-negative Matrix Factorization (NMF) method and implement various o↵shoots 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 o cials 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. IntroductionThe California Innocence Project (CIP), founded in 1999, is a clinical law school program hosted by California Western School of Law.Its core mission is to free wrongfully convicted prisoners, with the additional goals of reforming the criminal justice system and training upcoming law students.Since its inception, it has freed over 30 people who were wrongly incarcerated in the past years.After many years of operation, CIP has collected a significant quantity of unprocessed data.CIP receives approximately 1,500 new requests for assistance and over 4,000 pieces of digitized mail annually.This amount of information presents a challenge for the volunteers at CIP who manually process or classify these case files.At the same time, it also o↵ers an opportunity for data analysis that can reveal important information for CIP to understand the cases themselves and help CIP to evaluate its own decision process in handling cases.In this paper, we utilize Non-negative Matrix Factorization (NMF) method [11] and various o↵shoots of it to cluster CIP case files data and generate meaningful topics.Given that the nature of CIP data is mostly document based, after converting those documents (PDFs of handwriting) into texts, we can identify significant words that appear in those texts to form coherent lexical topics.Analyzing topics generated by each method allows us to answer various questions and address challenges that CIP has such as categorizing old case files, automating the categorization of incoming requests for assistance, and perhaps most importantly understanding why certain cases were chosen to pursue as opposed to others.Classical NMF [11] helps us understand large amount of data by o↵ering a low-rank representation of a data set.Semantic NMF [1] can take advantage of the hidden contextual and semantic meanings from a data set by training word-context dictionaries.Hierarchical NMF (HNMF) [7,10,19] can uncover the hierarchical structure of a data set at varying granularities by providing factorizations of the data set at many di↵erent ranks and identifying the relationship between the factorizations.Supervised NMF (SNMF) and semi-supervised NMF (SSNMF) [12] add a regularization term that utilizes class label information, the crime and decision type of each case, to form class-distinct topics and can also be used for classification.