Named Entity Recognition (NER)
Identifying and classifying named entities in text into categories like person, organization, location.
In-depth explanation
NER is a sequence labeling task that extracts and classifies entities from unstructured text. Common entity types include PERSON, ORGANIZATION, LOCATION, DATE, and MONEY. NER is fundamental to information extraction, question answering, and knowledge graph construction. Modern NER uses transformer-based models for state-of-the-art performance.
Examples
More in Natural Language Processing
Natural Language Processing (NLP)
The field of AI focused on enabling computers to understand, interpret, and generate human language.
Tokenization
Breaking text into smaller units (tokens) such as words, subwords, or characters.
Word Embedding
Dense vector representations of words that capture semantic meaning and relationships.
Sentiment Analysis
Determining the emotional tone or opinion expressed in text, typically positive, negative, or neutral.
BERT
Bidirectional Encoder Representations from Transformers, a pre-trained language model for NLP tasks.
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