Word embeddings are a type of representation that allows words to be mapped to dense vectors in a continuous vector space. This representation has become increasingly popular in natural language processing due to its ability to capture semantic and syntactic relationships between words.
Key Features of Word Embeddings
- Semantic Similarity: Words that are semantically similar tend to have similar vector representations.
- Syntactic Similarity: Words that appear in similar syntactic contexts tend to have similar vector representations.
- Disambiguation: Word embeddings can help disambiguate words with multiple meanings.
Types of Word Embeddings
- Word2Vec: A group of related models that are based on predictive models of context.
- GloVe: Global Vectors for Word Representation, which uses global matrix factorization to learn word vectors.
- FastText: An extension of Word2Vec that considers the n-grams of words to capture more context.
Applications of Word Embeddings
- Sentiment Analysis: Determining the sentiment of a text based on the sentiment of its words.
- Machine Translation: Translating text from one language to another.
- Text Classification: Categorizing text into predefined categories.
Further Reading
For more information on word embeddings, you can check out our comprehensive guide on Word Embeddings.
Word Embedding Visualization