This multilingual eDiscovery effort used text analytics and machine translation to effectively prioritize unreviewed materials. The result: 151% more productivity, saving time and money.
Semantic search will displace keyword search for e-discovery because it looks for meaning, not exact words, and enables searching in other languages.
How the KaDSci Team leveraged NLP to jump into second place in IARPA competition Basis Technology congratulates the authors of the peer-reviewed article “What do forecasting rationales reveal about thinking patterns of top geopolitical forecasters?”, which delves into the technical details of how the KaDSci team leapt from 27th place to 2nd place in three […]
How to automate mundane tasks and find relevant text using text embedding Numbers are great, because they are easy to compare, tabulate and examine. Text? Not so much. But text embeddings let one manipulate and compare the meaning behind words and text like numbers. Basically, text embeddings convert words, phrases, or even whole documents into […]