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Annotation with Active Learning and NLU

Make data preparation faster and more impactful with comprehensive and collaborative labeling

Working on natural language projects often requires understanding of domain specific terms and jargon. Some annotation activities even require high value and scarce subject matter experts to become the lead annotators and labelers. While some machine learning approaches can be used to perform information extraction, gold standard annotation quality requires domain specific datasets for success training and validation activities.

Quickly turn any user, including developers, knowledge engineers, computational linguists, and data scientists, into an annotation expert to scale your projects with a simple and easy to use interface that lets you explore and annotate a wide variety of different data point types. Expert.ai’s active learning annotation capabilities improve the efficiency and effectiveness of data labelling activities by employing machine learning to learn from existing annotations and create annotations suggestions to the user across the project corpus. Users can save time propagate annotations throughout an entire corpus.

By embedding the capabilities of expert.ai natural language understanding, expert.ai Platform annotation streamlines the annotation process. With out-of-the-box semantic text analytics, users can easily disambiguate extracted tokens, concepts, and entities within annotated documents.

Don’t drown in data labelling when there is a smarter choice to annotate unstructured text rapidly and accurately.

Key Features

  • Accelerate data labelling activities by letting the expert.ai platform identify the best documents to annotate and improve model quality
  • Active Learning where the system learns from already available annotations to suggest new annotations within the corpus
  • Use Natural Language Understanding suggestions based on inflections, compound words, synonyms to produce better annotations
  • Automatically pre-annotate documents using symbolic rules from other projects
  • Multiple annotators can work simultaneously on any project with tracking and validation checks
  • Fast-mode labelling allows for high volume entity annotation within a document
  • ‘One-click’ propagation of annotations across the whole corpus
  • Collaboration support with user changes tracking and validation checks
  • Import/export in BRAT standoff format
  • Drag and drop classification based on taxonomy
  • View annotation in the context of the original document file format