Efficient Vector Representation for Documents through Corruption

Minmin Chen

Nov 05, 2016 (modified: May 18, 2017) ICLR 2017 conference submission readers: everyone
  • Abstract: We present an efficient document representation learning framework, Document Vector through Corruption (Doc2VecC). Doc2VecC represents each document as a simple average of word embeddings. It ensures a representation generated as such captures the semantic meanings of the document during learning. A corruption model is included, which introduces a data-dependent regularization that favors informative or rare words while forcing the embeddings of common and non-discriminative ones to be close to zero. Doc2VecC produces significantly better word embeddings than Word2Vec. We compare Doc2VecC with several state-of-the-art document representation learning algorithms. The simple model architecture introduced by Doc2VecC matches or out-performs the state-of-the-art in generating high-quality document representations for sentiment analysis, document classification as well as semantic relatedness tasks. The simplicity of the model enables training on billions of words per hour on a single machine. At the same time, the model is very efficient in generating representations of unseen documents at test time.
  • TL;DR: a simple document representation learning framework that is very efficient to train and test
  • Conflicts: wustl.edu
  • Authorids: m.chen@criteo.com
  • Keywords: Natural language processing, Deep learning, Semi-Supervised Learning

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