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. |
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| Projects |
Idiap |
| Authors | |
| Crossref by |
Lebret_EACL_2014 |
| Added by: | [ADM] |
| Total mark: | 0 |
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