%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Lebret_EACL_2014,
         author = {Lebret, R{\'{e}}mi and Collobert, Ronan},
       projects = {Idiap},
          title = {Word Embeddings through Hellinger PCA},
      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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2014/Lebret_EACL_2014.pdf}
}



crossreferenced publications: 
@TECHREPORT{Lebret_Idiap-RR-29-2013,
         author = {Lebret, R{\'{e}}mi and Collobert, Ronan},
       projects = {Idiap},
          month = {8},
          title = {Word Embeddings through Hellinger PCA},
           type = {Idiap-RR},
         number = {Idiap-RR-29-2013},
           year = {2013},
    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.},
            pdf = {https://publications.idiap.ch/attachments/reports/2013/Lebret_Idiap-RR-29-2013.pdf}
}