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Supervised word mover's distance

Abstract:

Recently, a new document metric called the word mover’s distance (WMD) has been proposed with unprecedented results on kNN-based document classification. The WMD elevates high-quality word embeddings to a document metric by formulating the distance between two documents as an optimal transport problem between the embedded words. However, the document distances are entirely unsupervised and lack a mechanism to incorporate supervision when available. In this paper we propose an efficient techni...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
*Accepted Manuscript, Author's Original, Publisher's Version*

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Department of Computer Science
Role:
Author
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Publisher:
NIPS Foundation Publisher's website
Publication date:
2016-01-01
Pubs id:
pubs:924097
URN:
uri:0e65fe49-2205-4052-bc1f-df153f8d9d1a
UUID:
uuid:0e65fe49-2205-4052-bc1f-df153f8d9d1a
Local pid:
pubs:924097
Keywords:

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