What Word Embedding Meaning, Applications & Example
A numerical representation of words that captures semantic relationships.
What is Word Embedding?
Word embedding is a technique in natural language processing (NLP) that represents words as dense vectors of real numbers, where similar words have similar vector representations. These vectors capture semantic meanings of words based on their context in large text datasets, allowing algorithms to understand relationships between words.
How Word Embeddings Work
- Dimensionality Reduction : Unlike traditional one-hot encoding , which represents each word as a sparse vector with only one non-zero value, word embeddings reduce the dimensionality and represent words in a continuous vector space.
- Contextual Similarity: Words with similar meanings or usage patterns (e.g., “cat” and “dog”) are located closer together in the vector space, allowing models to capture semantic relationships.
- Training: Word embeddings are typically trained on large corpora of text using methods like Skip-gram or Continuous Bag of Words (CBOW), which learn to predict words based on their surrounding context.
Applications of Word Embeddings
- Text Classification: Word embeddings help models understand the meaning of text, improving the accuracy of classification tasks such as sentiment analysis or spam detection.
- Machine Translation: By representing words in vector space, embeddings facilitate the translation of text between languages, capturing similarities between words in different languages.
- Semantic Search: Word embeddings improve search engines by allowing them to understand queries more contextually, retrieving results based on meaning rather than just exact matches.
Example of Word Embedding
In an NLP task, word embeddings enable the model to understand that the words “king” and “queen” are more similar to each other than to the word “car,” by mapping them to nearby points in the vector space. This ability allows a model to perform tasks like analogy solving, where “king” is to “queen” as “man” is to “woman” based on their vector relationships.