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Word2Vec is a technique in Natural Language Processing (NLP) that transforms words into dense, continuous vector representations (embeddings) based on their context.
It uses two main models: Continuous Bag of Words (CBOW) and Skip-gram, both of which capture semantic relationships between words.
Word2Vec helps improve performance in various NLP tasks like sentiment analysis, machine translation, and text classification by providing more efficient, context-aware word representations compared to traditional sparse methods like one-hot encoding.
It also enables semantic generalization, such that similar words are mapped to similar vectors, and allows for transfer learning, where pre-trained embeddings can be adapted to specific domains or tasks. Additionally, Word2Vec is computationally efficient and scalable, making it suitable for processing large corpora of text.
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Word2Vec is a natural language processing (NLP) technique for embedding words (from a sentence or paragraph) and transforming these words into fixed-size vectors. Words that are similar in meaning and syntax are located close to each other in this vector space. This can aid in measuring word similarity and thus can be used in
sentiment analysis or topic modeling
Improving search relevance by matching words semantically, not just lexically
Understanding word semantics in translation systems and chatbots.