Luminoso, the company that turns unstructured text data into business critical insights, announced that it has promoted Henning Smith to Chief Technology Officer. "As the demand for Luminoso's text analytics applications have surged in recent years, our engineering and infrastructure teams have ensured our applications are exceeding customers' needs," said Adam Carte, CEO of Luminoso. "Henning Smith has been a stellar leader of our engineers during his tenure at Luminoso. As we continue to grow Luminoso globally, we're confident that Henning will ensure our customers continue to enjoy best-in-class text analytics solutions through both cloud and on-premise deployments." Henning has more than twenty years of experience leading and developing engineering teams in distributed locations, and managing global software development projects from inception through delivery.

While designing machine learning based text analytics applications, often, NLP data scientists manually determine which NLP features to use based upon their knowledge and experience with related problems. This results in increased efforts during feature engineering process and renders automated reuse of features across semantically related applications inherently difficult. In this paper, we argue for standardization in feature specification by outlining structure of a language for specifying NLP features and present an approach for their reuse across applications to increase likelihood of identifying optimal features.