GloVe (machine learning)

Short description: Algorithm for obtaining vector representations of words

GloVe, coined from Global Vectors, is a model for distributed word representation. The model is an unsupervised learning algorithm for obtaining vector representations for words. This is achieved by mapping words into a meaningful space where the distance between words is related to semantic similarity.[1] Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space. It is developed as an open-source project at Stanford[2] and was launched in 2014. As log-bilinear regression model for unsupervised learning of word representations, it combines the features of two model families, namely the global matrix factorization and local context window methods.[3]

## Contents

* 1 Applications
* 2 See also
* 3 References
* 4 External links

## Applications

GloVe can be used to find relations between words like synonyms, company-product relations, zip codes and cities, etc. However, the unsupervised learning algorithm is not effective in identifying homographs, i.e., words with the same spelling and different meanings. This is as the unsupervised learning algorithm calculates a single set of vectors for words with the same morphological structure.[4]The algorithm is also used by the SpaCy library to build semantic word embedding features, while computing the top list words that match with distance measures such as cosine similarity and Euclidean distance approach.[5] GloVe was also used as the word representation framework for the online and offline systems designed to detect psychological distress in patient interviews.[1]

## See also

* ELMo
* BERT
* Word2vec
* fastText
* Natural Language Processing

## References

1. ↑ 1.0 1.1 Abad, Alberto; Ortega, Alfonso; Teixeira, António; Mateo, Carmen; Hinarejos, Carlos; Perdigão, Fernando; Batista, Fernando; Mamede, Nuno (2016) (in en). Advances in Speech and Language Technologies for Iberian Languages: Third International Conference, IberSPEECH 2016, Lisbon, Portugal, November 23-25, 2016, Proceedings. Cham: Springer. pp. 165. ISBN 9783319491691.
2. ↑ GloVe: Global Vectors for Word Representation (pdf) "We use our insights to construct a new model for word representation which we call GloVe, for Global Vectors, because the global corpus statistics are captured directly by the model."
3. ↑ Kalajdziski, Slobodan (2018) (in en). ICT Innovations 2018. Engineering and Life Sciences. Cham: Springer. pp. 220. ISBN 9783030008246.
4. ↑ Wenig, Phillip (2019). "Creation of Sentence Embeddings Based on Topical Word Representations: An approach towards universal language understanding". Towards Data Science.
5. ↑ Singh, Mayank; Gupta, P. K.; Tyagi, Vipin; Flusser, Jan; Ören, Tuncer I. (2018). Advances in Computing and Data Sciences: Second International Conference, ICACDS 2018, Dehradun, India, April 20-21, 2018, Revised Selected Papers. Singapore: Springer. pp. 171. ISBN 9789811318122.

## External links

* GloVe
* Deeplearning4j GloVe[|permanent dead link|dead link}}]

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Natural language processing

General terms|

* Natural language understanding
* Text corpus
* Speech corpus
* Stopwords
* Bag-of-words
* AI-complete
* n-gram (Bigram, Trigram)

Text analysis|

* Text segmentation
* Part-of-speech tagging
* Text chunking
* Compound term processing
* Collocation extraction
* Stemming
* Lemmatisation
* Named-entity recognition
* Coreference resolution
* Sentiment analysis
* Concept mining
* Parsing
* Word-sense disambiguation
* Ontology learning
* Terminology extraction
* Textual entailment
* Truecasing

Automatic summarization|

* Multi-document summarization
* Sentence extraction
* Text simplification

Machine translation|

* Computer-assisted
* Example-based
* Rule-based
* Neural

Automatic identification
and data capture|

* Speech recognition
* Speech synthesis
* Optical character recognition
* Natural language generation

Topic model|

* Pachinko allocation
* Latent Dirichlet allocation
* Latent semantic analysis

Computer-assisted
reviewing|

* Automated essay scoring
* Concordancer
* Grammar checker
* Predictive text
* Spell checker
* Syntax guessing

Natural language
user interface|

* Automated online assistant
* Chatbot
* Interactive fiction
* Question answering
* Voice user interface

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