  In this paper we propose the application of feature hashing to create word
embeddings for natural language processing. Feature hashing has been used
successfully to create document vectors in related tasks like document
classification. In this work we show that feature hashing can be applied to
obtain word embeddings in linear time with the size of the data. The results
show that this algorithm, that does not need training, is able to capture the
semantic meaning of words. We compare the results against GloVe showing that
they are similar. As far as we know this is the first application of feature
hashing to the word embeddings problem and the results indicate this is a
scalable technique with practical results for NLP applications.

Computation and Language;Information Retrieval;Machine Learning