Sciweavers

Share
AAAI
2010

Kernelized Sorting for Natural Language Processing

11 years 3 months ago
Kernelized Sorting for Natural Language Processing
Kernelized sorting is an approach for matching objects from two sources (or domains) that does not require any prior notion of similarity between objects across the two sources. Unfortunately, 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 matching from parallel and comparable corpora, machine transliteration and even image processing. Empirically we show that, on these tasks, a semi-supervised variant of kernelized sorting outperforms matching canonical correlation analysis.
Jagadeesh Jagarlamudi, Seth Juarez, Hal Daum&eacut
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2010
Where AAAI
Authors Jagadeesh Jagarlamudi, Seth Juarez, Hal Daumé III
Comments (0)
books