2000 UseofSupportVectorLearningforCh

• (Kudoh & Matsumoto, 2000) ⇒ Taku Kudoh, and Yuji Matsumoto. (2000). “Use of Support Vector Learning for Chunk Identification.” In: Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7. doi:10.3115/1117601.1117635

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222000%22+Use+of+Support+Vector+Learning+for+Chunk+Identification
• http://dl.acm.org/citation.cfm?id=1117601.1117635&preflayout=flat#citedby

Quotes

Abstract

In this paper, we explore the use of Support Vector Machines (SVMs) for CoNLL-2000 shared task, chunk identification. SVMs are so-called large margin classifiers and are well-known as their good generalization performance. We investigate how SVMs with a very large number of features perform with the classification task of chunk labelling.

1 Introduction

In this paper, we explore the use of Support Vector Machines (SVMs) for CoNLL-2000 shared task, chunk identification. SVMs are so-called large margin classifiers and are well-known as their good generalization performance. We investigate how SVMs with a very large number of features perform with the classification task of chunk labelling.

2 Support Vector Machines

Support Vector Machines (SVMs), first introduced by Vapnik (Cortes and Vapnik, 1995; Vapnik, 1995), are relatively new learning approaches for solving two-class pattern recognition problems. SVMs are well-known for their good generalization performance, and have been applied to many pattern recognition problems. In the field of natural language processing, SVMs are applied to text categorization, and are reported to have achieved high accuracy without falling into over-fitting even with a large number of words taken as the features (Joachims, 1998; Taira and Haruno, 1999) First of all, let us define the training data which belongs to either positive or negative class as follows: (Xl, YX),..., (Xl, Yl) Xi 6 R n, Yi 6 { + 1, - 1}

…

4 Results

We have applied our proposed method to the test data of CoNLL-2000 shared task, while training with the complete training data. For the kernel function, we use the 2-nd polynomial function. We set the beam width N to 5 tentatively. SVMs training is carried out with the SVM light package, which is designed and optimized to handle large sparse feature vector and large numbers of training examples (Joachims, 2000; Joachims, 1999a). It took about 1 day to train 231 classifiers with PC-Linux (Celeron 500Mhz, 512MB).

…

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2000 UseofSupportVectorLearningforCh	Taku Kudo Yuji Matsumoto			Use of Support Vector Learning for Chunk Identification				10.3115/1117601.1117635		2000