Skip to Content

Word Embedding

Word embedding is used in natural language processing to translate words into numbers that computers can understand. Each word has a unique code represented as a vector (a list of numbers).

Each number in the vector represents a different aspect of the word’s meaning. Words with similar meanings end up with similar vectors, even if they’re spelled differently. For example, “king” and “queen” might have vectors close together because they share the concept of royalty. 

These vectors become tools for computers to analyze text. By comparing the vectors, machines can understand relationships between words, predict the next word in a sentence, and even perform tasks like translation and sentiment analysis.

The primary goal of word embedding is to capture the semantic and syntactic properties of words and their relationships with other words in the context of the surrounding text.

There are several methods for generating word embeddings, including:

  • Count-based methods, such as Word2Vec and GloVe, which generate word embeddings based on the co-occurrence of words in large text corpora.
  • Transformers, such as BERT, RoBERTa, and GPT, which generate context-rich word embeddings based on the transformer architecture and pre-training on large amounts of data.
  • FastText, which generates word embeddings based on the skip-gram model and sub-word information.

By using word embeddings, NLP models can capture the nuances of language and perform tasks that require understanding of context and meaning, leading to more accurate and effective results.

Related Terms

Learn More About Word Embedding