Data annotation

Data on its own is not enough to build a robust machine learning application. Data has to be enhanced through annotation. Data annotation or labeling refers to tagging or marking up predefined metadata elements of a dataset. We offer this service at scale and with utmost precision through our own Human Language Platform (HLP) and global HLP community.

Human-powered annotation at scale
Micro-task platform

Our proprietary micro-task platform is built to support a wide range of annotation use cases.

Quality assurance

We follow advanced quality assurance steps such as automated validation, spot-checks, and a vetting system to assign workers seniority.

Custom solutions

Do you have specific guidelines? We have full flexibility to work within your requirements and timeline.

Data types

Together with our global community and advanced technology, we have the capability to process text, audio and image data.

Data Annotation Types

Improve your machine learning models to create better AI systems, together with our global data community.

Named entity recognition (NER) tagging

Extracting structured entities from an unstructured block of text is an important step in the construction of many machine learning text algorithms. Data has to be prepared for algorithms to identify patterns within it. That’s where we come in. We can add meaningful metadata to the original dataset to create a rich layer of information to support machine learning.

Sentiment Analysis

It’s a challenge for AI algorithms to detect sentiment in data without the help of humans. That’s why human-annotation is a must to analyze sentiment. Sentiment analysis helps define whether a text is positive, negative or neutral by extracting particular words or phrases, providing helpful insights that drive effective business strategy.

Transcription Services

Create more value from your existing data by converting audio or visual elements into transcribed text.
Success Stories

Data Annotation to Optimize Searchability in E-Commerce

For a multinational e-commerce corporation, a community of 200+ TAUS contributors with product affinity in various product categories annotated data to optimize webshop functionalities such as searchability.

Domain-Specific Training Data Generation for SYSTRAN

After the training with TAUS datasets in the pandemic domain, the SYSTRAN engines improved on average by 18% across all twelve language pairs compared to the baseline engines.

Customization of Amazon Active Custom Translate with TAUS Data

The customization of Amazon Translate with TAUS Data always improved the BLEU score measured on the test sets by more than 6 BLEU points on average and 2 BLEU points at a minimum.

Let's connect

Talk to our experts to advance your ML systems with customized annotation or NER-tagging solutions designed specifically for your needs.