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 [BibTeX] [Marc21]
Word Embeddings through Hellinger PCA
Type of publication: Conference paper
Citation: Lebret_EACL_2014
Publication status: Published
Booktitle: 14th Conference of the European Chapter of the Association for Computational Linguistics
Year: 2014
Crossref: Lebret_Idiap-RR-29-2013:
Abstract: Word embeddings resulting from neural language models have been shown to be a great asset for a large variety of NLP tasks. However, such architecture might be difficult and time-consuming to train. 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 some well-known embeddings on named entity recognition and movie review tasks and show that we can reach similar or even better performance. Although deep learning is not really necessary for generating good word embeddings, we show that it can provide an easy way to adapt embeddings to specific tasks.
Projects Idiap
Authors Lebret, Rémi
Collobert, Ronan
Added by: [UNK]
Total mark: 0
  • Lebret_EACL_2014.pdf