Statistical methods for speech recognition
Statistical methods for speech recognition
Making large-scale support vector machine learning practical
Advances in kernel methods
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A study of thresholding strategies for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
User-System Cooperation in Document Annotation Based on Information Extraction
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A maximum entropy approach to information extraction from semi-structured and free text
Eighteenth national conference on Artificial intelligence
Relational learning techniques for natural language information extraction
Relational learning techniques for natural language information extraction
Machine learning for information extraction in informal domains
Machine learning for information extraction in informal domains
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Mixed-initiative development of language processing systems
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Journal of the American Society for Information Science and Technology - Intelligence and Security Informatics
Named entity recognition: a maximum entropy approach using global information
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
An empirical study of active learning with support vector machines for Japanese word segmentation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Rule writing or annotation: cost-efficient resource usage for base noun phrase chunking
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Sample Selection for Statistical Parsing
Computational Linguistics
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
Japanese dependency structure analysis based on support vector machines
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Learning a perceptron-based named entity chunker via online recognition feedback
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 using hundreds of thousands of features
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Exploring various knowledge in relation extraction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
The Journal of Machine Learning Research
Relational learning via propositional algorithms: an information extraction case study
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Using uneven margins SVM and perceptron for information extraction
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
SVM based learning system for information extraction
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Topic model methods for automatically identifying out-of-scope resources
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
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
Experiments on summary-based opinion classification
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Towards semantic annotation supported by dependency linguistics and ILP
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
Exploring techniques for rationale extraction from existing documents
Proceedings of the 34th International Conference on Software Engineering
Can text summaries help predict ratings? a case study of movie reviews
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
An approach to automatic music band member detection based on supervised learning
AMR'11 Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
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Support Vector Machines (SVM) have been used successfully in many Natural Language Processing (NLP) tasks. The novel contribution of this paper is in investigating two techniques for making SVM more suitable for language learning tasks. Firstly, we propose an SVM with uneven margins (SVMUM) model to deal with the problem of imbalanced training data. Secondly, SVM active learning is employed in order to alleviate the difficulty in obtaining labelled training data. The algorithms are presented and evaluated on several Information Extraction (IE) tasks, where they achieved better performance than the standard SVM and the SVM with passive learning, respectively. Moreover, by combining SVMUM with the active learning algorithm, we achieve the best reported results on the seminars and jobs corpora, which are benchmark data sets used for evaluation and comparison of machine learning algorithms for IE. In addition, we also evaluate the token based classification framework for IE with three different entity tagging schemes. In comparison to previous methods dealing with the same problems, our methods are both effective and efficient, which are valuable features for real-world applications. Due to the similarity in the formulation of the learning problem for IE and for other NLP tasks, the two techniques are likely to be beneficial in a wide range of applications1.