Continuous Bag-of-Words Word2Vec is an architecture for creating word embeddings that uses $n$ future words as well as $n$ past words to create a word embedding. The objective function for CBOW is:
$$ J_\theta = \frac{1}{T}\sum^{T}_{t=1}\log{p}\left(w_{t}\mid{w}_{t-n},\ldots,w_{t-1}, w_{t+1},\ldots,w_{t+n}\right) $$
In the CBOW model, the distributed representations of context are used to predict the word in the middle of the window. This contrasts with Skip-gram Word2Vec where the distributed representation of the input word is used to predict the context.
Source: Efficient Estimation of Word Representations in Vector SpacePaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Dependency Parsing | 1 | 11.11% |
Lemmatization | 1 | 11.11% |
NER | 1 | 11.11% |
Information Retrieval | 1 | 11.11% |
Retrieval | 1 | 11.11% |
Specificity | 1 | 11.11% |
Sentence | 1 | 11.11% |
Natural Language Inference | 1 | 11.11% |
Word Similarity | 1 | 11.11% |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |