What is Text Annotation?
To train AI models, algorithms use enormous amounts of tagged data as part of a larger data labelling procedure. A metadata tag is used to indicate up properties of a dataset during the annotation process. That data includes tags that indicate criteria such as keywords, phrases, or sentences in text annotation. Text annotation can also entail marking various feelings in text, such as "angry" or "sarcastic," to teach the computer how to discern human intent or emotion behind words in certain applications.
Types of Annotations
There are many different forms of text annotations, including sentiment, intent, semantic, and connection annotations. These options are accessible in a number of different human languages.
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Sentiment Annotation
Sentiment annotation evaluates attitudes and emotions behind a text by labeling that text as positive, negative, or neutral.
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Intent Annotation
Intent annotation analyzes the need or desire behind a text, classifying it into several categories, such as request, command, or confirmation.
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Semantic Annotation
Semantic annotation attaches various tags to text that reference concepts and entities, such as people, places, or topics.
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Relationship Annotation
Relationship annotation seeks to draw various relationships between different parts of your document. Typical tasks include dependency resolution and coreference resolution.