GloVeStanford NLP
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LexVecAlexandre Salle
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Related Products
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About
GloVe (Global Vectors for Word Representation) is an unsupervised learning algorithm developed by the Stanford NLP Group to obtain vector representations for words. It constructs word embeddings by analyzing global word-word co-occurrence statistics from a given corpus, resulting in vector spaces where the geometric relationships reflect semantic similarities and differences among words. A notable feature of GloVe is its ability to capture linear substructures within the word vector space, enabling vector arithmetic to express relationships. The model is trained on the non-zero entries of a global word-word co-occurrence matrix, which records how frequently pairs of words appear together in a corpus. This approach efficiently leverages statistical information by focusing on significant co-occurrences, leading to meaningful word representations. Pre-trained word vectors are available for various corpora, including Wikipedia 2014.
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About
LexVec is a word embedding model that achieves state-of-the-art results in multiple natural language processing tasks by factorizing the Positive Pointwise Mutual Information (PPMI) matrix using stochastic gradient descent. This approach assigns heavier penalties for errors on frequent co-occurrences while accounting for negative co-occurrences. Pre-trained vectors are available, including a common crawl dataset with 58 billion tokens and 2 million words in 300 dimensions, and an English Wikipedia 2015 + NewsCrawl dataset with 7 billion tokens and 368,999 words in 300 dimensions. Evaluations demonstrate that LexVec matches or outperforms other models like word2vec in terms of word similarity and analogy tasks. The implementation is open source under the MIT License and is available on GitHub.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Data scientists in search of a solution to enhance their natural language processing models with word embeddings that capture global statistical information from large text corpora
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Audience
Computational linguists and NLP researchers searching for a tool to improve their semantic analysis and language modeling
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
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Pricing
Free
Free Version
Free Trial
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Pricing
Free
Free Version
Free Trial
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationStanford NLP
United States
nlp.stanford.edu/projects/glove/
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Company InformationAlexandre Salle
Brazil
github.com/alexandres/lexvec
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Integrations
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Integrations
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