Semantic Role Labeling
Semantic Role Labeling



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Prodigy is a scriptable annotation tool so efficient that data scientists can do the annotation themselves, enabling a new level of rapid iteration. Today’s transfer learning technologies mean you can train production-quality models with very few examples. With Prodigy you can take full advantage of modern machine learning by adopting a more agile approach to data collection. Prodigy brings together state-of-the-art insights from machine learning and user experience. The web application is powerful, extensible and follows modern UX principles.



Image for Text Classification · Prodigy · An annotation tool for AI, Machine Learning & NLP

Text Classification · Prodigy · An annotation tool for AI, Machine Learning & NLP

... Whether you’re doing intent detection, information extraction, semantic role labeling or sentiment analysis, Prodigy provides easy, flexible and powerful annotation options. Active learning keeps you efficient even if your classes are heavily imbalanced. ...

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„Semantic Role Labeling“

Semantic Role Labeling (SRL) is a natural language processing task in which a system identifies and classifies the semantic roles of each word or phrase in a sentence, such as the subject, object, and predicate. SRL is used to better understand the meaning of a sentence and to identify relationships between words in a sentence.