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Low-degree Polynomial Mapping of NLP Data and Features Condensing by Hashing
Date Issued
2011
Date
2011
Author(s)
Chung, Po-Han
Abstract
Recently, many people handle natural language processing (NLP) tasks via support vector machines (SVM) with polynomial kernels. However, kernel computation is time consuming. Chang et al. (2010) have proposed mapping data by low-degree polynomial functions and applying fast linear-SVM methods. For data with many features, they have considered condensing data to effectively solve some memory and computational difficulties. In this thesis, we investigate Chang et al.''s methods and give implementation details. We conduct experiments on four NLP tasks to show the viability of our implementation.
Subjects
Natural language processing
Support vector machine
Polynomial mapping
Type
thesis
File(s)
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Name
ntu-100-R98944031-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):63d05301acae8e331a4e32440a997d3b