Automate Your Complex Text Annotation Process

Text annotation automation tools are being used across various domains like social media, news, and law due to increasing quantities of the unstructured nature of free-text data.

When you’re using complex text annotation tools, the metadata needed includes tags that highlight certain attributes such as phrases, keywords, or sentences and assigns them a label like proper names, sentiment, or intention.

Simply put, automated text annotation appends notes to the text with different criteria based on your specific requirements and use case.

Deliver >94% Text Annotation Accuracy for Multi Level Classification with Wrk

In the past, annotation tools would still require substantial manual intervention from experts to verify complex text and manually label the embedded information. When you’re pulling data from several different sources, the text you receive can arrive fairly messy. The pulled information then needs to be cleaned and structured for your team to put to good use. Doing this entire process manually can be tedious and time-consuming, not to mention, it requires a lot of supervision and oversight, which can almost duplicate efforts.

At Wrk, we take a hybrid approach where we combine APIs, RPAs, and several other AI inputs with human intervention—through a skilled human community of workers.

The Wrkflow Request

Our client needed to find a scalable solution that wouldn’t duplicate their team’s efforts and their overhead. With our Wrkflow, we were able to help our client clean up their initial input files and output structured classified results with a high accuracy rate.

Download this case study to see how we developed a Wrkflow to systematically deliver accurate annotation of complex text for multi-level, nested classification.

Check out the results in our Case Study today!