Combining human annotation and your machine predictions to deliver high quality annotated text for your NLP Projects
First, you upload plain text to annotate or a JSON of custom tokens, spans, and predictions. Quickly configure classes for annotators with custom class instructions and in-tool help functionality. Create flexible test questions to evaluate annotators on comprehension and accuracy. Finally, launch jobs and aggregate correct labels from multiple annotators with an inter-annotator agreement score. See where your model required human correction.
Human labelers quickly assign class labels to individual tokens and spans for entity extraction and parts of speech labeling. Machine Learning Assisted Text Annotation can be used for chatbots, search science, document understanding and other use cases. Leverage your own tokenizers or use spaCy, NLTK or Stanford in Figure Eight’s platform to create tokens quickly in English and 18 other languages. Bring your own model predictions to help annotators increase precision, recall, and speed.
Upload your predictions for human annotator review. Every annotation is recorded as being “Machine” or “Human” labeled to track class level accuracy, recall, and precision. Reduce annotator bias with test questions that can test for true negatives and false positives from the model or the annotator. Feed low-performing corrected labels back into model for retraining to reduce error rate quickly and cost-effectively.
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