GloVe
GloVe, coined from Global Vectors, is a model for distributed word representation. The model is an
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
References
- ^ ISBN 9783319491691.
- ^ 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."
- ISBN 9783030008246.
- ^ Wenig, Phillip (2019). "Creation of Sentence Embeddings Based on Topical Word Representations: An approach towards universal language understanding". Towards Data Science.
- ISBN 9789811318122.
External links
- GloVe
- Deeplearning4j GloVe Archived 2019-02-02 at the Wayback Machine