Word Mover’s Embedding: From Word2Vec to Document Embedding

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Authors Pin-Yu Chen, Avinash Balakrishnan, Kun Xu, Lingfei Wu, Ian E. H. Yen, Fangli Xu, Michael J. Witbrock, Pradeep Ravikumar
Journal/Conference Name EMNLP 2018 10
Paper Category
Paper Abstract While the celebrated Word2Vec technique yields semantically rich representations for individual words, there has been relatively less success in extending to generate unsupervised sentences or documents embeddings. Recent work has demonstrated that a distance measure between documents called \emph{Word Mover's Distance} (WMD) that aligns semantically similar words, yields unprecedented KNN classification accuracy. However, WMD is expensive to compute, and it is hard to extend its use beyond a KNN classifier. In this paper, we propose the \emph{Word Mover's Embedding } (WME), a novel approach to building an unsupervised document (sentence) embedding from pre-trained word embeddings. In our experiments on 9 benchmark text classification datasets and 22 textual similarity tasks, the proposed technique consistently matches or outperforms state-of-the-art techniques, with significantly higher accuracy on problems of short length.
Date of publication 2018
Code Programming Language C
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