# Guided Semi-Supervised Non-negative Matrix Factorization on Legal Documents

arXiv: https://arxiv.org/abs/2201.13324 · HTML: https://mlsanity.com/paper/2201.13324/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 31 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [Macro F1 and Macro F1](https://mlsanity.com/paper/1911.03347/) (2019)
- [A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models](https://mlsanity.com/paper/2010.15036/) (2020)
- [Semi-supervised NMF Models for Topic Modeling in Learning Tasks](https://mlsanity.com/paper/2010.07956/) (2020)
- [On a Guided Nonnegative Matrix Factorization](https://mlsanity.com/paper/2010.11365/) (2020)
- [COVID-19 Literature Topic-Based Search via Hierarchical NMF](https://mlsanity.com/paper/2009.09074/) (2020)
- [Analysis of Legal Documents via Non-negative Matrix Factorization Methods](https://mlsanity.com/paper/2104.14028/) (2021)
- [Topic supervised non-negative matrix factorization](https://mlsanity.com/paper/1706.05084/) (2017)

## Built on by (most cited first, 5 of 5)

- [Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix Factorization](https://mlsanity.com/paper/2502.20364/) (2025)
- [Finding Rule-Interpretable Non-Negative Data Representation](https://mlsanity.com/paper/2206.01483/) (2022)
- [Continuous Semi-Supervised Nonnegative Matrix Factorization](https://mlsanity.com/paper/2212.09858/) (2022)
- [Efficient Topic Extraction via Graph-Based Labeling: A Lightweight Alternative to Deep Models](https://mlsanity.com/paper/2511.04248/) (2025)
- [Trustworthy Domain-Specific AI for Structured Knowledge Retrieval and Reasoning](https://mlsanity.com/paper/2610.08894/) (2026)

## Abstract (arXiv)

Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori information such as labels or important features, methods have been developed to perform classification and topic modeling tasks; however, most methods that can perform both do not allow for guidance of the topics or features. In this paper, we propose a method, namely Guided Semi-Supervised Non-negative Matrix Factorization (GSSNMF), that performs both classification and topic modeling by incorporating supervision from both pre-assigned document class labels and user-designed seed words. We test the performance of this method through its application to legal documents provided by the California Innocence Project, a nonprofit that works to free innocent convicted persons and reform the justice system. The results show that our proposed method improves both classification accuracy and topic coherence in comparison to past methods like Semi-Supervised Non-negative Matrix Factorization (SSNMF) and Guided Non-negative Matrix Factorization (Guided NMF).