Named Entity Recognition

What do you mean by Named Entity Recognition?

Named entity recognition (NER) is a technique within artificial intelligence (AI) and natural language processing (NLP) that involves identifying, tagging, and categorizing named entities in data. These entities can include cities, celebrities, brands, and more. NER also recognizes and categorizes the noun type represented by an entity, such as geography, person, or business, which aids in topic clustering.

Using NER, a machine learning model can identify words that are written differently or misspelled, ensuring they are not excluded during the tagging process. For instance, NER enables a social listening software to recognize that variations like Faceb00k and FB both refer to Facebook and can be tagged as a social network.

NER algorithms employ statistical models to grasp the semantic and contextual understanding of words. Knowledge graphs further establish relationships between entities, contributing to a comprehensive comprehension of the data. This aspect makes NER crucial in sentiment analysis.

During sentiment analysis, NER enables sentiment analysis algorithms to assign a sentiment value to each entity identified. These actionable insights assist brands in making targeted improvements to their strategies, such as developing engaging content, optimizing customer care responses, creating better-targeted advertisements, and more.

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