Measuring the VC-dimension of a learning machine
Neural Computation
WordNet: a lexical database for English
Communications of the ACM
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A hierarchical approach to wrapper induction
Proceedings of the third annual conference on Autonomous Agents
Relational learning of pattern-match rules for information extraction
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
A simple, fast, and effective rule learner
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
Wrapper induction: efficiency and expressiveness
Artificial Intelligence - Special issue on Intelligent internet systems
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
A Theory-Refinement Approach to Information Extraction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Information Extraction: Techniques and Challenges
SCIE '97 International Summer School on Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology
A Brief Introduction to Boosting
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
XWRAP: An XML-Enabled Wrapper Construction System for Web Information Sources
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Relational learning techniques for natural language information extraction
Relational learning techniques for natural language information extraction
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
SRI International FASTUS system: MUC-6 test results and analysis
MUC6 '95 Proceedings of the 6th conference on Message understanding
Immediate-head parsing for language models
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Representing sentence structure in hidden Markov models for information extraction
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
CRYSTAL inducing a conceptual dictionary
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Combining Information Extraction Systems Using Voting and Stacked Generalization
The Journal of Machine Learning Research
Cooperative CG-Wrappers for Web Content Extraction
ICCS '07 Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications
Mining employment market via text block detection and adaptive cross-domain information extraction
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Hi-index | 0.00 |
In this paper, we examine an important recent rule-based information extraction (IE) technique named Boosted Wrapper Induction (BWI) by conducting experiments on a wider variety of tasks than previously studied, including tasks using several collections of natural text documents. We investigate systematically how each algorithmic component of BWI, in particular boosting, contributes to its success. We show that the benefit of boosting arises from the ability to reweight examples to learn specific rules (resulting in high precision) combined with the ability to continue learning rules after all positive examples have been covered (resulting in high recall). As a quantitative indicator of the regularity of an extraction task, we propose a new measure that we call the SWI ratio. We show that this measure is a good predictor of IE success and a useful tool for analyzing IE tasks. Based on these results, we analyze the strengths and limitations of BWI. Specifically, we explain limitations in the information made available, and in the representations used. We also investigate the consequences of the fact that confidence values returned during extraction are not true probabilities. Next, we investigate the benefits of including grammatical and semantic information for natural text documents, as well as parse tree and attribute-value information for XML and HTML documents. We show experimentally that incorporating even limited grammatical information can increase the regularity of natural text extraction tasks, resulting in improved performance. We conclude with proposals for enriching the representational power of BWI and other IE methods to exploit these and other types of regularities.