[Apple | Organization] and [Oranges | Fruit]: How to Evaluate NLP Tools for Entity Extraction
[Apple | Organization] and [Oranges | Fruit]: How to Evaluate NLP Tools for Entity Extraction


You have some documents and you want to extract information, but which NLP tool or library to use? You have many from which to choose but how to evaluate which is best? Evaluating NLP tools is not a straightforward exercise since differences in output between tools often prevent direct comparison. NLP tools based on different underlying technologies will tag text differently, extract different sets of entities and classify those entities differently. Basis Technology often performs evaluations between disparate NLP tools and encounters these challenges. Learn how we handle them to produce meaningful scoring of tools.


Gil leads the engineering team responsible for text analytics including existing products and new technology initiatives. He has nearly 30 years of experience in developing software and leading engineering teams, including work done at Curl (now part of Sumitomo Corporation), GTECH (now part of IGT PLC) and Constant Contact. Gil holds a BS in computer science from Cornell University, an MA in Liberal Arts from Harvard University and a certificate in management from MIT’s Sloan School of Management.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from - Youtube
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google