More talks in the program:
16:00 - 16:30
The challenges of Machine Learning (ML) start with collecting training data. First, labeled data resources are scarce. Second, the increasing complexity and changing nature of industries, such as healthcare or cyber-security, require the constant knowledge and verification of subject-matter experts (SMEs). In the context of natural language, this knowledge comes in the form of text annotations, for instance entity or document labels.
Collaborations between data analytics/AI professionals and SMEs often fail, or are just non existent. This is partially due to the lack of annotation tools, which could simplify the communication and reduce the time required to label data.
Today we present tagtog, a collaborative text annotation tool to bridge this gap. In this session, we will walk through the web interface, showing how to manually create or validate text annotations. We will also show how SMEs can teach and deploy ML models at scale, only by providing feedback in an easy-to-use interface. We will use these models to annotate automatically and to demonstrate how this approach can create large amounts of training data, in a time-efficient manner, and adapted to the specific subject domain.