AI GlossaryWord EmbeddingENTRY — Natural Language Processing
[Natural Language Processing]
Word Embedding.
Dense vector representations of words that capture semantic meaning and relationships.
In-depth explanation
01Word 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
02EX. 01
Word2Vec embeddings
EX. 02
GloVe vectors
EX. 03
BERT contextual embeddings
More in Natural Language Processing
0301BERTBidirectional Encoder Representations from Transformers, a pre-trained language model for NLP tasks.02Named Entity Recognition (NER)Identifying and classifying named entities in text into categories like person, organization, location.03Natural Language Processing (NLP)The field of AI focused on enabling computers to understand, interpret, and generate human language.04Sentiment AnalysisDetermining the emotional tone or opinion expressed in text, typically positive, negative, or neutral.05TokenizationBreaking text into smaller units (tokens) such as words, subwords, or characters.
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