The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Use of support vector learning for chunk identification
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
An empirical study of Chinese chunking
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Frequent words' grammar information in Chinese chunking
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Exploiting chunk-level features to improve phrase chunking
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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The paper presents a method of Chinese chunk recognition based on Support Vector Machines (SVMs) plus Sigmoid. It is well known that SVMs are binary classifiers which achieve the best performance in many tasks. However, directly applying binary classifiers in the task of Chinese chunking will face the dilemmas that either two or more different class labels are given to a single unlabeled constituent, or no class labels are given for some unlabeled constituents. Employing sigmoid functions is a method of extracting probabilities (class/input) from SVMs outputs, which is helpful to post-processing of classification. These probabilities are then used to resolve the dilemmas. We compare our method based on SVMs plus Sigmoid with methods based only on SVMs. The experiments show that significant improvements have been achieved.