“doc2vec: Distributed Representations of Sentences and Documents”, 2014-05-16 (; similar):
Many machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts, one of the most common fixed-length features is bag-of-words. Despite their popularity, bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. For example, “powerful”, “strong” and “Paris” are equally distant.
In this paper, we propose Paragraph Vector, an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents. Our algorithm represents each document by a dense vector which is trained to predict words in the document. Its construction gives our algorithm the potential to overcome the weaknesses of bag-of-words models.
Empirical results show that Paragraph Vectors outperform bag-of-words models as well as other techniques for text representations.
Finally, we achieve new state-of-the-art results on several text classification and sentiment analysis tasks.
[doc2vec seems to have been somewhat notorious for not working as well as reported; a followup paper suggests there was a problem with the data preprocessing: “In our experiments, to match the results from (2014), we followed the suggestion by Quoc Le to use hierarchical softmax instead of negative sampling. However, this produces the 92.6% accuracy result only when the training and test data are not shuffled. Thus, we consider this result to be invalid.”]