Paper: Fast Methods For Kernel-Based Text Analysis

ACL ID P03-1004
Title Fast Methods For Kernel-Based Text Analysis
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2003
Authors

Kernel-based learning (e.g. , Support Vec- tor Machines) has been successfully ap- plied to many hard problems in Natural Language Processing (NLP). In NLP, al- though feature combinations are crucial to improving performance, they are heuris- tically selected. Kernel methods change this situation. The merit of the kernel methods is that effective feature combina- tion is implicitly expanded without loss of generality and increasing the compu- tational costs. Kernel-based text analysis shows an excellent performance in terms in accuracy; however, these methods are usually too slow to apply to large-scale text analysis. In this paper, we extend a Basket Mining algorithm to convert a kernel-based classifier into a simple and fast linear classifier. Experimental results on English BaseNP Chun...