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A Practical Guide to Hybrid Natural Language ProcessingAligning Embedding Spaces and Applications for Knowledge Graphs

A Practical Guide to Hybrid Natural Language Processing: Aligning Embedding Spaces and... [In previous chapters we have seen a variety of ways to train models to derive embedding spaces for words and concepts and other nodes in knowledge graphs. As you often do not have control over the full training procedure, you may find yourself with several embedding spaces which have (conceptually) overlapping vocabularies. How can you best combine such embedding spaces?. In this chapter we look at various techniques for aligning disparate embedding spaces. This is particularly useful in hybrid settings like when using embedding spaces for knowledge graph curation and interlinking.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Practical Guide to Hybrid Natural Language ProcessingAligning Embedding Spaces and Applications for Knowledge Graphs

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Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2020
ISBN
978-3-030-44829-5
Pages
151 –164
DOI
10.1007/978-3-030-44830-1_9
Publisher site
See Chapter on Publisher Site

Abstract

[In previous chapters we have seen a variety of ways to train models to derive embedding spaces for words and concepts and other nodes in knowledge graphs. As you often do not have control over the full training procedure, you may find yourself with several embedding spaces which have (conceptually) overlapping vocabularies. How can you best combine such embedding spaces?. In this chapter we look at various techniques for aligning disparate embedding spaces. This is particularly useful in hybrid settings like when using embedding spaces for knowledge graph curation and interlinking.]

Published: Jun 17, 2020

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