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Word2vec
Sign in to saveWord2vec is a technique in natural language processing for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. Word2vec was developed by Tomáš Mikolov, Kai Chen, Greg Corrado, Ilya Sutskever and Jeff Dean at Google, and published in 2013.
Key facts
- Software.name
- word2vec
- Software.author
- Google AI
- Software.repo
- https://code.google.com/archive/p/word2vec/
- Software.license
- Apache-2.0
via Wikipedia infobox
Article
23 sectionsContents
- Approach
- Mathematical details
- Continuous bag-of-words (CBOW)
- Skip-gram
- History
- Parameterization
- Training algorithm
- Sub-sampling
- Dimensionality
- Context window
- Extensions
- doc2vec
- top2vec
- BioVectors
- Radiology and intelligent word embeddings (IWE)
- Analysis
- Preservation of semantic and syntactic relationships
- Assessing the quality of a model
- Parameters and model quality
- See also
- References
- External links
- Implementations
Word2vec is a technique in natural language processing for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. Word2vec was developed by Tomáš Mikolov, Kai Chen, Greg Corrado, Ilya Sutskever and Jeff Dean at Google, and published in 2013.
Word2vec represents a word as a high-dimension vector of numbers which capture relationships between words. In particular, words which appear in similar contexts are mapped to vectors which are nearby as measured by cosine similarity. This indicates the level of semantic similarity between the words, so for example the vectors for walk and ran are nearby, as are those for "but" and "however", and "Berlin" and "Germany".