Word Embedding
Dense vector representations of words that capture semantic meaning and relationships.
In-depth explanation
Word embeddings map words to continuous vector spaces where similar words are close together. Word2Vec, GloVe, and FastText learn embeddings from large text corpora. Embeddings capture semantic relationships: vector("king") - vector("man") + vector("woman") ≈ vector("queen"). Modern models use contextual embeddings that change based on surrounding words.
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.
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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