COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Detection of IUPAC and IUPAC-like chemical names
Bioinformatics
Maximum Margin Active Learning for Sequence Labeling with Different Length
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Self-Teaching Semantic Annotation Method for Knowledge Discovery from Text
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
A web survey on the use of active learning to support annotation of text data
HLT '09 Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing
An analysis of active learning strategies for sequence labeling tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
On privacy preservation in text and document-based active learning for named entity recognition
Proceedings of the ACM first international workshop on Privacy and anonymity for very large databases
Semi-Supervised Sequence Labeling with Self-Learned Features
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Semi-supervised active learning for sequence labeling
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
| Hi-index | 0.00 |
In recent years, many NLP systems and tasks are developed using machine learning methods. In order to achieve the best performance, these systems are generally trained on a large human annotated corpus. Since annotating such corpora is a very expensive and time-consuming procedure, manually annotating corpora is become one of the significant issues in many text based tasks such as text mining, semantic annotation, Named Entity Recognition and generally Information Extraction. Semi-supervised Learning and Active Learning are two distinct approaches that deal with reduction of labeling costs. Based on their natures, Active and semi-supervised learning can produce better results when they are jointly applied. In this paper we propose a combined Semi-Supervised and Active Learning approach for Sequence Labeling which extremely reduces manual annotation cost in a way that only highly uncertain tokens need to be manually labeled and other sequences and subsequences are labeled automatically. The proposed approach reduces manual annotation cost around 90% compare with a supervised learning and 30% in contrast with a similar fully active learning approach. Conditional Random Field CRF is chosen as the underlying learning model due to its promising performance in many sequence labeling tasks. In addition we proposed a confidence measure based on the model's variance reduction that reaches a considerable accuracy for finding informative samples.