Thread by @LionbridgeAI

Here's a beginner's guide to 5 types of text annotation for #MachineLearning, including:

- Entity annotation
- Entity linking
- Text classification
- Sentiment analysis
- Linguistic annotation

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#AI #NLP #DataScience

Entity Annotation:

This is the act of locating, extracting, and tagging entities in text. Examples are tagging proper names, locating and labeling key phrases, and identifying parts of speech.

Entity linking:

is the process of connecting named entities to larger repositories of data about them (Wikipedia, for example). This is helpful for disambiguating named entities, and providing further information.

Text classification:

is the process of analyzing a text and classifying it based on a predetermined list of categories. This is useful for sorting categories (e.g. news articles) and categorizing products within an inventory.

Sentiment annotation:

Also known as sentiment analysis, this is the labelling of emotion, opinion, or sentiment within a body of text. This is helpful for analyzing social media trends, and better understanding customer feedback.

Linguistic annotation:

is the process of tagging language data in text. This means identifying and flagging grammatical, semantic, or phonetic elements such as grammatical word function or phonetics.

For more examples of text annotation and more detailed explanations, check out our comprehensive article below:

lionbridge.ai/articles/an-in…