Word2vec Skill
Word2Vec is a popular unsupervised learning algorithm for generating word embeddings, which are dense vector representations of words in a continuous vector space. Developed by researchers at Google, including Tomas Mikolov, Word2Vec aims to capture semantic relationships between words based on their contextual usage in large text corpora.The key idea behind Word2Vec is to learn distributed representations of words by predicting words within their contextual window in a given text. The model considers the distributional hypothesis, assuming that words with similar meanings tend to appear in similar contexts.Word2Vec introduces two architectures for training word embeddings: Continuous Bag of Words (CBOW) and Skip-gram. CBOW predicts a target word based on its context, while Skip-gram predicts the context words given a target word. Both architectures leverage a shallow neural network to learn the word embeddings.The resulting word vectors encode semantic relationships, and words with similar meanings have similar vector representations. Word2Vec embeddings have proven valuable in natural language processing tasks, including text classification, sentiment analysis, and machine translation. Pre-trained Word2Vec embeddings are often used as feature vectors in downstream NLP applications, providing semantic information that enhances the performance of various language-related tasks.
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