Word2Vec is a popular word embedding technique introduced by Tomas Mikolov et al. at Google in 2013. It is designed to learn dense vector representations of words from large amounts of unlabeled text data. Word2Vec is based on the idea that words that appear in similar contexts are likely to have similar meanings.
There are two main architectures for training Word2Vec models: Continuous Bag-of-Words (CBOW) and Skip-gram. In CBOW, the model predicts the current word based on its context, while in Skip-gram, the model predicts the surrounding context words given the current word. Both architectures involve training a neural network to learn the word embeddings.
During training, the Word2Vec model assigns a unique vector to each word in the vocabulary. These word vectors are often referred to as “word embeddings" or “word representations". The dimensionality of the word vectors is a hyperparameter that needs to be specified before training. Typically, the dimensionality is set to a relatively small value, such as 100 or 300.
The training process involves iterating through the text corpus and updating the word vectors based on their context. The model learns to optimize the word vectors by minimizing the prediction error. The resulting word embeddings capture the semantic and syntactic relationships between words, with similar words having similar vector representations.
One of the key benefits of Word2Vec is its ability to capture the semantic meaning of words. By leveraging the context in which words appear, Word2Vec can represent words with similar meanings as vectors that are close together in the vector space. This allows for tasks such as word similarity, word analogy, and even identifying relationships between words.
Word2Vec models are typically trained on large amounts of text data, such as Wikipedia articles or entire books. Pretrained Word2Vec models are also available, allowing for transfer learning, where the pre-trained embeddings can be used as a starting point for downstream NLP tasks.
Word2Vec has been widely adopted in various natural language processing applications. It has proven to be effective in tasks such as sentiment analysis, text classification, named entity recognition, machine translation, and more. The compact and meaningful word representations learned by Word2Vec have greatly contributed to the advancements in NLP and have become an essential tool for understanding and processing natural language.
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.