Tokenization
Breaking text into smaller units (tokens) such as words, subwords, or characters.
In-depth explanation
Tokenization is the first step in NLP pipelines, converting raw text into a sequence of tokens that models can process. Word tokenization splits on spaces; subword tokenization (like BPE, WordPiece) handles unknown words better. Each token is then typically mapped to a numerical ID. Tokenization choices significantly impact model 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.
Word Embedding
Dense vector representations of words that capture semantic meaning and relationships.
Named Entity Recognition (NER)
Identifying and classifying named entities in text into categories like person, organization, location.
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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