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 [BibTeX] [Marc21]
Word Embeddings through Hellinger PCA
Type of publication: Idiap-RR
Citation: Lebret_Idiap-RR-29-2013
Number: Idiap-RR-29-2013
Year: 2013
Month: 8
Institution: Idiap
Abstract: Word embeddings resulting from neural lan- guage models have been shown to be successful for a large variety of NLP tasks. However, such architecture might be difficult to train and time-consuming. Instead, we propose to drastically simplify the word embeddings computation through a Hellinger PCA of the word co-occurence matrix. We compare those new word embeddings with the Collobert and Weston (2008) embeddings on several NLP tasks and show that we can reach similar or even better performance.
Projects Idiap
Authors Lebret, RĂ©mi
Collobert, Ronan
Crossref by Lebret_EACL_2014
Added by: [ADM]
Total mark: 0
  • Lebret_Idiap-RR-29-2013.pdf (MD5: 92cc554c93b93e817b5f2684e9e50fe3)