36 citations · 105 across the 17 of their papers we have counts for
17 papers
Exploiting the Randomness of Large Language Models (LLM) in Text Classification Tasks: Locating Privileged Documents in Legal Matters
Keith Huffman, Jianping Zhang, Nathaniel Huber-Fliflet +2
In legal matters, text classification models are most often used to filter through large datasets in search of documents that meet certain pre-selected criteria like relevance to a…
Leveraging Machine Learning and Large Language Models for Automated Image Clustering and Description in Legal Discovery
Qiang Mao, Fusheng Wei, Robert Neary +4
The rapid increase in digital image creation and retention presents substantial challenges during legal discovery, digital archive, and content management. Corporations and legal t…
A Comparative Study of Retrieval Methods in Azure AI Search
Qiang Mao, Han Qin, Robert Neary +4
Increasingly, attorneys are interested in moving beyond keyword and semantic search to improve the efficiency of how they find key information during a document review task. Large…
Detecting Privileged Documents by Ranking Connected Network Entities
Jianping Zhang, Han Qin, Nathaniel Huber-Fliflet
This paper presents a link analysis approach for identifying privileged documents by constructing a network of human entities derived from email header metadata. Entities are class…
Empirical Evaluation of Embedding Models in the Context of Text Classification in Document Review in Construction Delay Disputes
Fusheng Wei, Robert Neary, Han Qin +2
Text embeddings are numerical representations of text data, where words, phrases, or entire documents are converted into vectors of real numbers. These embeddings capture semantic…
Explainable Text Classification Techniques in Legal Document Review: Locating Rationales without Using Human Annotated Training Text Snippets
Christian Mahoney, Peter Gronvall, Nathaniel Huber-Fliflet +1
US corporations regularly spend millions of dollars reviewing electronically-stored documents in legal matters. Recently, attorneys apply text classification to efficiently cull ma…
MTTM: Metamorphic Testing for Textual Content Moderation Software
Wenxuan Wang, Jen-tse Huang, Weibin Wu +5
The exponential growth of social media platforms such as Twitter and Facebook has revolutionized textual communication and textual content publication in human society. However, th…
AEON: A Method for Automatic Evaluation of NLP Test Cases
Jen-tse Huang, Jianping Zhang, Wenxuan Wang +3
Due to the labor-intensive nature of manual test oracle construction, various automated testing techniques have been proposed to enhance the reliability of Natural Language Process…
Improving Adversarial Transferability via Neuron Attribution-Based Attacks
Jianping Zhang, Weibin Wu, Jen-tse Huang +4
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. It is thus imperative to devise effective attack algorithms to identify the deficiencies of DNNs bef…
CNN Application in Detection of Privileged Documents in Legal Document Review
Rishi Chhatwal, Robert Keeling, Peter Gronvall +3
Protecting privileged communications and data from disclosure is paramount for legal teams. Legal advice, such as attorney-client communications or litigation strategy are typicall…
A Framework for Explainable Text Classification in Legal Document Review
Christian J. Mahoney, Jianping Zhang, Nathaniel Huber-Fliflet +2
Companies regularly spend millions of dollars producing electronically-stored documents in legal matters. Recently, parties on both sides of the 'legal aisle' are accepting the use…
Empirical Comparisons of CNN with Other Learning Algorithms for Text Classification in Legal Document Review
Robert Keeling, Rishi Chhatwal, Nathaniel Huber-Fliflet +5
Research has shown that Convolutional Neural Networks (CNN) can be effectively applied to text classification as part of a predictive coding protocol. That said, most research to d…
Evaluation of Seed Set Selection Approaches and Active Learning Strategies in Predictive Coding
Christian J. Mahoney, Nathaniel Huber-Fliflet, Haozhen Zhao +3
Active learning is a popular methodology in text classification - known in the legal domain as "predictive coding" or "Technology Assisted Review" or "TAR" - due to its potential t…
An Empirical Study of the Application of Machine Learning and Keyword Terms Methodologies to Privilege-Document Review Projects in Legal Matters
Peter Gronvall, Nathaniel Huber-Fliflet, Jianping Zhang +3
Protecting privileged communications and data from disclosure is paramount for legal teams. Unrestricted legal advice, such as attorney-client communications or litigation strategy…
Explainable Text Classification in Legal Document Review A Case Study of Explainable Predictive Coding
Rishi Chhatwal, Peter Gronvall, Nathaniel Huber-Fliflet +3
In today's legal environment, lawsuits and regulatory investigations require companies to embark upon increasingly intensive data-focused engagements to identify, collect and analy…
Empirical Evaluations of Active Learning Strategies in Legal Document Review
Rishi Chhatwal, Nathaniel Huber-Fliflet, Robert Keeling +2
One type of machine learning, text classification, is now regularly applied in the legal matters involving voluminous document populations because it can reduce the time and expens…
Empirical Evaluations of Preprocessing Parameters' Impact on Predictive Coding's Effectiveness
Rishi Chhatwal, Nathaniel Huber-Fliflet, Robert Keeling +2
Predictive coding, once used in only a small fraction of legal and business matters, is now widely deployed to quickly cull through increasingly vast amounts of data and reduce the…