Text annotation tools assume that their user's goal is to create a labeled corpus. However, users view annotation as a necessary evil on the way to deliver business value through NLP. Thus an annotation tool should optimize for the throughput of the global NLP process, not only the productivity of individual annotators. LightTag is a text annotation tool designed and built on that principle. This paper shares our design rationale, data modeling choices, and user interface decisions then illustrates how those choices serve the full NLP lifecycle.

Machine learning is the secret sauce that allows us to use computers to automate tasks in powerful new ways. However, there are a lot of steps that must go right for the ML to work: ideas must be mined from huge amounts of data, clean sample data must be provided to train models, and models must be managed and maintained over time. Here are three startups looking to simplify some of these aspects of the ML lifecycle. Before a data scientist can build a machine learning model, she must first identify patterns in the real world. There are various ways to identify patterns.

LightTag, a newly launched startup from a former NLP researcher at Citi, has built a "text annotation platform" designed to assist data scientists who need to quickly create training data for their AI systems. It's a classic picks'n' shovels move, in that the bootstrapped Berlin-based company is hoping to take advantage of the current boom in AI development. Specifically, LightTag aims to solve one of the main bottlenecks of'deep learning'-based AI development: what you get out is only as good as the labeled data you put in. The problem, however, is that labelling data is laborious, and since it's a job carried out by teams of humans it is prone to inaccuracy and inconsistency. LightTag's team-based workflow, clever UI, and in-built quality controls is an attempt to mitigate this.