Add to ORKGTraditional methods of discovery for civil lawsuits take too many human hours. Reading hundreds of thousands of documents is inefficient and prone to human error. Every document is either relevant or not relevant to a case, but needs to be read and understand by a lawyer or paralegal for that to happen. We use semi-supervised machine learning in order to incrementally learn the criteria that make a document relevant to a particular discovery request. Our features include semantic meaning gathered from Latent Semantic Analysis and metadata, and our algorithm of choice is a Random Forest. We also include a graphical interface so a lawyer can use our system
The discovery process is regularly capturing millions of pages of documents. Electronic storage is m...
A paper document processing system is an information system component which transforms information o...
Knowledge Discovery in Databases (KDD), also known as data mining, focuses on the computerized explo...
Traditional methods of discovery for civil lawsuits take too many human hours. Reading hundreds of t...
The present work proposes a method for the automatic extraction of textual elements within documents...
This Article explores the application of machine learning techniques within the practice of law. Bro...
Abstract. Legal text retrieval traditionally relies upon external knowledge sources such as thesauri...
In litigation in the US, the parties are obligated to pro-duce to one another, when requested, those...
We present a methodology for document processing that exploits logic-based machine learning techniqu...
Electronic discovery, or e-discovery, refers to the discovery of electronically stored documents and...
This dissertation describes a knowledge-based system for classifying documents based upon the layout...
This paper surveys three basic legal-text analytic techniques—ML, network diagrams, and question ans...
In this work I detail the compilation of a unique corpus of Norwegian court decisions. I utilize thi...
Many questions facing legal scholars and practitioners can be answered only by analysing and interr...
Abstract: Significant obstacles must be overcome if machine learning techniques are to be applied in...
The discovery process is regularly capturing millions of pages of documents. Electronic storage is m...
A paper document processing system is an information system component which transforms information o...
Knowledge Discovery in Databases (KDD), also known as data mining, focuses on the computerized explo...
Traditional methods of discovery for civil lawsuits take too many human hours. Reading hundreds of t...
The present work proposes a method for the automatic extraction of textual elements within documents...
This Article explores the application of machine learning techniques within the practice of law. Bro...
Abstract. Legal text retrieval traditionally relies upon external knowledge sources such as thesauri...
In litigation in the US, the parties are obligated to pro-duce to one another, when requested, those...
We present a methodology for document processing that exploits logic-based machine learning techniqu...
Electronic discovery, or e-discovery, refers to the discovery of electronically stored documents and...
This dissertation describes a knowledge-based system for classifying documents based upon the layout...
This paper surveys three basic legal-text analytic techniques—ML, network diagrams, and question ans...
In this work I detail the compilation of a unique corpus of Norwegian court decisions. I utilize thi...
Many questions facing legal scholars and practitioners can be answered only by analysing and interr...
Abstract: Significant obstacles must be overcome if machine learning techniques are to be applied in...
The discovery process is regularly capturing millions of pages of documents. Electronic storage is m...
A paper document processing system is an information system component which transforms information o...
Knowledge Discovery in Databases (KDD), also known as data mining, focuses on the computerized explo...
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