A high-performance, scalable text processing pipeline for eDiscovery is outlined. The classification module of the pipeline is based on the random forest model which is fast, flexible and allows for relevance scoring and feature importance coupled with high-accuracy results. The feature selection approach combines natural language processing with legal domain input, and is based on regular expressions, which allows for linguistic variation and subtle fine-tuning. These two components of the pipeline...
