Are you paying a fortune to translate files before e-discovery? If your files are in multiple languages, why not first reduce the number of files that need expensive translation?
Until now, keyword search has been the main method for finding relevant documents from mountains of data in e-discovery. But keyword search requires huge lists of terms, which has the effect of limiting its scope and precision. It catches too little, e.g., by focusing on a specific spelling while missing key alternatives, like different word forms and other ways of saying the same thing. More critical, keyword search doesn’t understand context or meaning.
What if your documents are in multiple languages? You’d need those keyword lists — translated — for all languages. And you’d have to be confident the keyword terms were correctly translated and interpreted for each language.
Now you can. Through a partnership between BasisTech and Ai Translate by LSI, you can use a powerful, AI-driven methodology that will find and extract key documents containing your search phrases or concepts across multiple languages. This process uses cross-lingual semantics to find the precise meaning-based content relevant to your case. It keeps your key concepts from being lost in machine translation while boosting the speed and accuracy of e-discovery. So, rather than translating reams of documents first, you can focus translation efforts on the files that are actually pertinent to your case.
In this on demand webinar by Eugene Reyes, BasisTech and Jason Boro, Linguistic Systems Inc., you’ll learn:
- About system that uses advanced semantics to do e-discovery in the native language of the document
- How to produce more accurate and defensible results, while saving time and money
- How to quickly identify and prioritize the items that need full translation
Eugene Reyes, Federal Solutions Engineer, BasisTech
Eugene Reyes Federal Solutions Engineer BasisTechEugene has decades of experience in software engineering, mainly in several offices within the United States Intelligence Community (IC) and Department of Defense (DOD). Eugene has been part of multiple applied research and development organizations, focusing on advancing text analytics through various Human Language Technologies (HLT), such as Latent Semantic Indexing (LSI), entity extraction, and machine translation. At BasisTech, he created Semantic Studio to demonstrate existing BasisTech product capabilities. Prior to joining BasisTech, Eugene was a Software Engineer with Numbers Place, Inc., where he architected and developed an HLT solution for the IC. Eugene obtained a B.S. in computer science and a minor in political science at Virginia Polytechnic Institute and State University.
Jason Boro, Esq., Strategic Partnership Development Manager, Linguistic Systems Inc.
Jason Boro, Linguistic Systems IncAt Linguistic Systems, Inc., Jason manages research and development of new products. He possesses over 15 years of e-discovery experience. Prior to LSI, Jason spent 4 years with the U.S. Attorney’s Office in Massachusetts. He has also practiced law in Massachusetts; in the Federal District Court for Massachusetts; and in Washington, DC. Jason holds a graduate degree in Linguistics from the University of Connecticut. He also completed a course of study and conducted research elaborating psychological models for parsing sentence grammar while supplying a theoretical account of the maturation of innate syntax.