From Word Embeddings To Document Distances. Kusner, M., Sun, Y., Kolkin, N., & Weinberger, K. In International Conference on Machine Learning, pages 957-966.
From Word Embeddings To Document Distances [link]Paper  abstract   bibtex   
We present the Word Mover’s Distance (WMD), a novel distance function between text documents. Our work is based on recent results in word embeddings that learn semantically meaningful representatio...
@inproceedings{kusnerWordEmbeddingsDocument2015,
  langid = {english},
  title = {From {{Word Embeddings To Document Distances}}},
  url = {http://proceedings.mlr.press/v37/kusnerb15.html},
  abstract = {We present the Word Mover’s Distance (WMD), a novel distance function between text documents. Our work is based on recent results in word embeddings that learn semantically meaningful representatio...},
  eventtitle = {International {{Conference}} on {{Machine Learning}}},
  booktitle = {International {{Conference}} on {{Machine Learning}}},
  urldate = {2019-02-19},
  date = {2015-06-01},
  pages = {957-966},
  author = {Kusner, Matt and Sun, Yu and Kolkin, Nicholas and Weinberger, Kilian},
  file = {/home/dimitri/Nextcloud/Zotero/storage/A2XMBEIG/Kusner et al. - 2015 - From Word Embeddings To Document Distances.pdf;/home/dimitri/Nextcloud/Zotero/storage/7SHEHA48/kusnerb15.html}
}
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