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Word2vec

Word2vec

Word2vec is a group of related models for learning word embeddings.

Word2vec is a model for learning vector representation of words called word embeddings. It transforms words in numerical form which then can be used in natural language processing and machine learning applications.

Word2Vec computes distributed vector representation of words. Distributed representations make the generalization to novel patterns easier and model estimation more robust. Distributed vector representation is used in natural language processing applications such as named entity recognition, disambiguation, parsing, tagging and machine translation.

Word2vec is a computationally effective predictive model for learning word embeddings from raw text. It uses unsupervised learning models, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model. Algorithmically, these models are the same, except that CBOW predicts target words from source context words, while the skip-gram does the inverse and predicts source context-words from the target words.

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Caviar’s Word2Vec Tagging For Menu Item Recommendations

Christopher Skeels and Yash Patel

Corpus specificity in LSA and Word2vec: the role of out-of-domain documents

Edgar Altszyler, Mariano Sigman, Diego Fernandez Slezak

Academic paper

Determining the Characteristic Vocabulary for a Specialized Dictionary using Word2vec and a Directed Crawler

Gregory Grefenstette TAO, Lawrence Muchemi TAO

Academic paper

Distributed Representations of Words and Phrases and their Compositionality

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean

Efficient Estimation of Word Representations in Vector Space

Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean

Word and Phrase Translation with word2vec

Stefan Jansen

Academic paper

word2vec Parameter Learning Explained

Xin Rong

Word2vec Tutorial

Radim Řehůřek

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