Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Scenario customization for information extraction
Scenario customization for information extraction
An improved extraction pattern representation model for automatic IE pattern acquisition
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Counter-training in discovery of semantic patterns
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Unsupervised named-entity extraction from the web: an experimental study
Artificial Intelligence
Espresso: leveraging generic patterns for automatically harvesting semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
Improving semi-supervised acquisition of relation extraction patterns
IEBeyondDoc '06 Proceedings of the Workshop on Information Extraction Beyond The Document
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Analysis and improvement of minimally supervised machine learning for relation extraction
NLDB'09 Proceedings of the 14th international conference on Applications of Natural Language to Information Systems
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
Conversational agents in a virtual world
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Minimally supervised domain-adaptive parse reranking for relation extraction
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
Large-Scale learning of relation-extraction rules with distant supervision from the web
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
Assessing sparse information extraction using semantic contexts
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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This paper presents a new approach to improving relation extraction based on minimally supervised learning. By adding some limited closed-world knowledge for confidence estimation of learned rules to the usual seed data, the precision of relation extraction can be considerably improved. Starting from an existing baseline system we demonstrate that utilizing limited closed world knowledge can effectively eliminate "dangerous" or plainly wrong rules during the bootstrapping process. The new method improves the reliability of the confidence estimation and the precision value of the extracted instances. Although recall suffers to a certain degree depending on the domain and the selected settings, the overall performance measured by F-score considerably improves. Finally we validate the adaptability of the best ranking method to a new domain and obtain promising results.