Kernelized Sorting for Natural Language Processing

  • Jagarlamudi J
  • Juarez S
  • Daumé III H
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Abstract

Kernelized sorting is an approach for matching objects from two sources (or domains) that does not require any prior no- tion of similarity between objects across the two sources. Un- fortunately, this technique is highly sensitive to initialization and high dimensional data. We present variants of kernelized sorting to increase its robustness and performance on several Natural Language Processing (NLP) tasks: document match- ing from parallel and comparable corpora, machine transliter- ation and even image processing. Empirically we show that, on these tasks, a semi-supervised variant of kernelized sorting outperforms matching canonical correlation analysis.

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  • PUI: 359833449
  • ISBN: 9781577354659
  • SGR: 77958535142
  • SCOPUS: 2-s2.0-77958535142

Authors

  • Jagadeesh Jagarlamudi

  • Seth Juarez

  • Hal Daumé III

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