acl acl2013 acl2013-294 acl2013-294-reference knowledge-graph by maker-knowledge-mining

294 acl-2013-Re-embedding words


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Author: Igor Labutov ; Hod Lipson

Abstract: We present a fast method for re-purposing existing semantic word vectors to improve performance in a supervised task. Recently, with an increase in computing resources, it became possible to learn rich word embeddings from massive amounts of unlabeled data. However, some methods take days or weeks to learn good embeddings, and some are notoriously difficult to train. We propose a method that takes as input an existing embedding, some labeled data, and produces an embedding in the same space, but with a better predictive performance in the supervised task. We show improvement on the task of sentiment classification with re- spect to several baselines, and observe that the approach is most useful when the training set is sufficiently small.


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