Robust document image understanding technologies
Proceedings of the 1st ACM workshop on Hardcopy document processing
Distributed higher order association rule mining using information extracted from textual data
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Automated criminal link analysis based on domain knowledge: Research Articles
Journal of the American Society for Information Science and Technology
Adapting svm for data sparseness and imbalance: A case study in information extraction
Natural Language Engineering
Regular expression learning for information extraction
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Improving recall of regular expressions for information extraction
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Automatic string replace by examples
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Learning regular expressions to template-based FAQ retrieval systems
Knowledge-Based Systems
| Hi-index | 0.00 |
In this article we present a semi-supervised active learning algorithm for pattern discovery in information extraction from textual data. The patterns are reduced regular expressions composed of various characteristics of features useful in information extraction. Our major contribution is a semi-supervised learning algorithm that extracts information from a set of examples labeled as relevant or irrelevant to a given attribute. The approach is semi-supervised because it does not require precise labeling of the exact location of features in the training data. This significantly reduces the effort needed to develop a training set. An active learning algorithm is used to assist the semi-supervised learning algorithm to further reduce the training set development effort. The active learning algorithm is seeded with a single positive example of a given attribute. The context of the seed is used to automatically identify candidates for additional positive examples of the given attribute. Candidate examples are manually pruned during the active learning phase, and our semi-supervised learning algorithm automatically discovers reduced regular expressions for each attribute. We have successfully applied this learning technique in the extraction of textual features from police incident reports, university crime reports, and patents. The performance of our algorithm compares favorably with competitive extraction systems being used in criminal justice information systems. © 2005 Wiley Periodicals, Inc.