2 citations · 2 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Empirical Evaluations of Seed Set Selection Strategies for Predictive Coding
Christian J. Mahoney, Nathaniel Huber-Fliflet, Katie Jensen +3
Training documents have a significant impact on the performance of predictive models in the legal domain. Yet, there is limited research that explores the effectiveness of the trai…