Difference between revisions of "Word2Vec"
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* [[Global Vectors for Word Representation (GloVe)]] | * [[Global Vectors for Word Representation (GloVe)]] | ||
* [[Bag-of-Words (BoW)]] | * [[Bag-of-Words (BoW)]] | ||
| − | * [[Continuous Bag-of-Words ( | + | * [[Continuous Bag-of-Words (CBoW)]] |
| + | * [[Natural Language Processing (NLP)#Similarity |Similarity]] | ||
| + | * [http://projector.tensorflow.org/ Embedding Projector] | ||
| + | * [http://pathmind.com/wiki/word2vec A Beginner's Guide to Word2Vec and Neural Word Embeddings | Chris Nicholson - A.I. Wiki pathmind] | ||
| + | * [http://towardsdatascience.com/introduction-to-word-embedding-and-word2vec-652d0c2060fa Introduction to Word Embedding and Word2Vec | Dhruvil Karani - Towards Data Science - Medium] | ||
| + | * [http://arxiv.org/pdf/1310.4546.pdf Distributed Representations of Words and Phrases | ||
| + | and their Compositionality | Tomas Mikolov - Google] | ||
| + | |||
| + | a shallow, two-layer neural networks which is trained to reconstruct linguistic contexts of words. It takes as its input a large corpus of words and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. | ||
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| + | http://miro.medium.com/max/1394/0*XMW5mf81LSHodnTi.png | ||
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<youtube>ERibwqs9p38</youtube> | <youtube>ERibwqs9p38</youtube> | ||
Revision as of 23:14, 3 May 2020
Youtube search... ...Google search
- Natural Language Processing (NLP)
- Doc2Vec
- Node2Vec
- Skip-Gram
- Global Vectors for Word Representation (GloVe)
- Bag-of-Words (BoW)
- Continuous Bag-of-Words (CBoW)
- Similarity
- Embedding Projector
- A Beginner's Guide to Word2Vec and Neural Word Embeddings | Chris Nicholson - A.I. Wiki pathmind
- Introduction to Word Embedding and Word2Vec | Dhruvil Karani - Towards Data Science - Medium
- [http://arxiv.org/pdf/1310.4546.pdf Distributed Representations of Words and Phrases
and their Compositionality | Tomas Mikolov - Google]
a shallow, two-layer neural networks which is trained to reconstruct linguistic contexts of words. It takes as its input a large corpus of words and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space.
