REPORT Lebret_Idiap-RR-44-2013/IDIAP Is Deep Learning Really Necessary for Word Embeddings? Lebret, Rémi Legrand, Joël Collobert, Ronan EXTERNAL http://publications.idiap.ch/attachments/reports/2013/Lebret_Idiap-RR-44-2013.pdf PUBLIC Idiap-RR-44-2013 2013 Idiap December 2013 Accepted to NIPS Deep Learning Workshop Word embeddings resulting from neural language 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 sim- plify 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 NER 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.