What Word2Vec Meaning, Applications & Example
Algorithm for creating word embeddings.
What is Word2Vec?
Word2Vec is a technique used to transform words into continuous vector representations in a high-dimensional space. This technique captures semantic relationships between words by mapping them to vectors, where words with similar meanings are close together in the vector space. Word2Vec is typically used in natural language processing (NLP) tasks.
Types of Word2Vec Models
- Continuous Bag of Words (CBOW): Predicts the target word from a context (surrounding words). It is faster and often works better when the corpus is smaller.
- Skip-gram: Predicts the context (surrounding words) from a target word. This model is better for larger corpora and tends to generate higher-quality word vectors.
Applications of Word2Vec
- Text Classification: Word2Vec helps represent words numerically, making it easier to classify text into categories.
- Sentiment Analysis : By capturing semantic meanings, Word2Vec can improve the accuracy of predicting sentiment in text.
- Recommendation Systems: Word2Vec can be used to find similar items based on textual descriptions.
Example of Word2Vec
For example, in a sentence like “The cat sits on the mat,” Word2Vec would map “cat,” “sits,” “on,” and “mat” to vectors. Words like “cat” and “dog” would have similar vector representations, as they belong to the same category of animals. This helps in applications like finding similar words or text clustering .