TY - CHAP

T1 - Multi-view discriminative sequential learning

AU - Brefeld, Ulf

AU - Büscher, Christoph

AU - Scheffer, Tobias

N1 - Conference code: 16

PY - 2005/1/1

Y1 - 2005/1/1

N2 - Discriminative learning techniques for sequential data have proven to be more effective than generative models for named entity recognition, information extraction, and other tasks of discrimination. However, semi-supervised learning mechanisms that utilize inexpensive unlabeled sequences in addition to few labeled sequences - such as the Baum-Welch algorithm - are available only for generative models. The multi-view approach is based on the principle of maximizing the consensus among multiple independent hypotheses; we develop this principle into a semi-supervised hidden Markov perceptron, and a semi-supervised hidden Markov support vector learning algorithm. Experiments reveal that the resulting procedures utilize unlabeled data effectively and discriminate more accurately than their purely supervised counterparts.

AB - Discriminative learning techniques for sequential data have proven to be more effective than generative models for named entity recognition, information extraction, and other tasks of discrimination. However, semi-supervised learning mechanisms that utilize inexpensive unlabeled sequences in addition to few labeled sequences - such as the Baum-Welch algorithm - are available only for generative models. The multi-view approach is based on the principle of maximizing the consensus among multiple independent hypotheses; we develop this principle into a semi-supervised hidden Markov perceptron, and a semi-supervised hidden Markov support vector learning algorithm. Experiments reveal that the resulting procedures utilize unlabeled data effectively and discriminate more accurately than their purely supervised counterparts.

KW - Informatics

KW - Unlabeled Data

KW - Neural Information Processing System

KW - Name Entity Recognition

KW - Entity Recognition

KW - Word Sense Disambiguation

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=33646415916&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/f5846d33-6389-3b3a-b23a-bdde947af373/

U2 - 10.1007/11564096_11

DO - 10.1007/11564096_11

M3 - Article in conference proceedings

AN - SCOPUS:33646415916

SN - 978-3-540-29243-2

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 60

EP - 71

BT - Machine Learning: ECML 2005

PB - Springer

T2 - 16th European Conference on Machine Learning

Y2 - 3 October 2005 through 7 October 2005

ER -