Communications of the ACM
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
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
Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Information Extraction: Techniques and Challenges
SCIE '97 International Summer School on Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Unsupervised named-entity extraction from the web: an experimental study
Artificial Intelligence
The role of syntax in information extraction
TIPSTER '96 Proceedings of a workshop on held at Vienna, Virginia: May 6-8, 1996
Discovering relations among named entities from large corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Low-Cost Supervision for Multiple-Source Attribute Extraction
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Assessing sparse information extraction using semantic contexts
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Web extraction systems attempt to use the immense amount of unlabeled text in the Web in order to create large lists of entities and relations. Unlike traditional IE methods, the Web extraction systems do not label every mention of the target entity or relation, instead focusing on extracting as many different instances as possible while keeping the precision of the resulting list reasonably high. URES is a Web relation extraction system that learns powerful extraction patterns from unlabeled text, using short descriptions of the target relations and their attributes. The performance of URES is further enhanced by classifying its output instances using the properties of the extracted patterns. The features we use for classification and the trained classification model are independent from the target relation, which we demonstrate in a series of experiments. In this paper we show how the introduction of a simple rule based NER can boost the performance of URES on a variety of relations. We also compare the performance of URES to the performance of the state-of-the-art KnowItAll system, and to the performance of its pattern learning component, which uses a simpler and less powerful pattern language than URES.