Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Text chunking based on a generalization of winnow
The Journal of Machine Learning Research
Tagging English text with a probabilistic model
Computational Linguistics
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Solving large scale linear prediction problems using stochastic gradient descent algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Weakly supervised natural language learning without redundant views
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Named entity recognition with a maximum entropy approach
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Named entity recognition through classifier combination
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Named entity recognition with character-level models
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
A robust risk minimization based named entity recognition system
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Information Extraction: Distilling Structured Data from Unstructured Text
Queue - Social Computing
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Bootstrapping without the boot
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Exploiting domain structure for named entity recognition
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Reducing weight undertraining in structured discriminative learning
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
A robust multilingual portable phrase chunking system
Expert Systems with Applications: An International Journal
Improving discriminative sequential learning by discovering important association of statistics
ACM Transactions on Asian Language Information Processing (TALIP)
Estimation and use of uncertainty in pseudo-relevance feedback
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Overview and semantic issues of text mining
ACM SIGMOD Record
Robust and efficient multiclass SVM models for phrase pattern recognition
Pattern Recognition
Towards Machine Learning on the Semantic Web
Uncertainty Reasoning for the Semantic Web I
Applying alternating structure optimization to word sense disambiguation
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
A context pattern induction method for named entity extraction
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
A fast boosting-based learner for feature-rich tagging and chunking
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Design challenges and misconceptions in named entity recognition
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A self-training approach to cost sensitive uncertainty sampling
Machine Learning
Semi-supervised learning for blog classification
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Quadratic features and deep architectures for chunking
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Can one language bootstrap the other: a case study on event extraction
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
One class per named entity: exploiting unlabeled text for named entity recognition
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Locating complex named entities in web text
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Data selection in semi-supervised learning for name tagging
IEBeyondDoc '06 Proceedings of the Workshop on Information Extraction Beyond The Document
A risk minimization framework for domain adaptation
Proceedings of the 18th ACM conference on Information and knowledge management
Domain adaptation via transfer component analysis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Analysis and robust extraction of changing named entities
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
Cross-domain sentiment classification via spectral feature alignment
Proceedings of the 19th international conference on World wide web
Verb class discovery from rich syntactic data
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Word representations: a simple and general method for semi-supervised learning
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Cross-language text classification using structural correspondence learning
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Joint training and decoding using virtual nodes for cascaded segmentation and tagging tasks
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Learning condensed feature representations from large unsupervised data sets for supervised learning
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Localized factor models for multi-context recommendation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Cross-Lingual Adaptation Using Structural Correspondence Learning
ACM Transactions on Intelligent Systems and Technology (TIST)
Chinese chunking with tri-training learning
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
Efficient and robust phrase chunking using support vector machines
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
Pairwise cross-domain factor model for heterogeneous transfer ranking
Proceedings of the fifth ACM international conference on Web search and data mining
Syntactic chunking across different corpora
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
A named entity extraction using word information repeatedly collected from unlabeled data
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Nudging the envelope of direct transfer methods for multilingual named entity recognition
WILS '12 Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure
Learning multilingual named entity recognition from Wikipedia
Artificial Intelligence
Stock price prediction based on a complex interrelation network of economic factors
Engineering Applications of Artificial Intelligence
Prediction of movement direction in crude oil prices based on semi-supervised learning
Decision Support Systems
Double-bootstrapping source data selection for instance-based transfer learning
Pattern Recognition Letters
A self-trained semisupervised SVM approach to the remote sensing land cover classification
Computers & Geosciences
Robust predictive model for evaluating breast cancer survivability
Engineering Applications of Artificial Intelligence
Multilingual joint parsing of syntactic and semantic dependencies with a latent variable model
Computational Linguistics
Aligned-Parallel-Corpora Based Semi-Supervised Learning for Arabic Mention Detection
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Sharpened graph ensemble for semi-supervised learning
Intelligent Data Analysis
| Hi-index | 0.00 |
In machine learning, whether one can build a more accurate classifier by using unlabeled data (semi-supervised learning) is an important issue. Although a number of semi-supervised methods have been proposed, their effectiveness on NLP tasks is not always clear. This paper presents a novel semi-supervised method that employs a learning paradigm which we call structural learning. The idea is to find "what good classifiers are like" by learning from thousands of automatically generated auxiliary classification problems on unlabeled data. By doing so, the common predictive structure shared by the multiple classification problems can be discovered, which can then be used to improve performance on the target problem. The method produces performance higher than the previous best results on CoNLL'00 syntactic chunking and CoNLL'03 named entity chunking (English and German).