Preemptive information extraction using unrestricted relation discovery
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
On-demand information extraction
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
StatSnowball: a statistical approach to extracting entity relationships
Proceedings of the 18th international conference on World wide web
An Incremental Knowledge Acquisition Method for Improving Duplicate Invoices Detection
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Efficient Knowledge Acquisition for Extracting Temporal Relations
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Open information extraction using Wikipedia
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
RDRCE: combining machine learning and knowledge acquisition
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Combining different summarization techniques for legal text
HYBRID '12 Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data
Knowledge acquisition for categorization of legal case reports
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
Improving open information extraction for informal web documents with ripple-down rules
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
Improving the performance of a named entity recognition system with knowledge acquisition
EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
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The Web contains a massive amount of information embedded in text and obtaining information from Web text is a major research challenge. One research focus is Open Information Extraction aimed at developing relation-independent information extraction. Open Information Extraction (OIE) systems seek to extract all potential relations from the text rather than extracting a few pre-defined relations. Existing OIE systems such as TEXTRUNNER usually take a machine learning based approach which requires large volumes of training data. This paper presents a Ripple-Down Rules Open Information Extraction system based on processing example cases and manually adding rules when needed. The key advantages of this approach are that it can handle the freer writing style that occurs in Web documents and can correct errors introduced by natural language pre-processing tools, whereas systems like TEXTRUNNER depend on the quality of the entity-tagging preprocessing in the training data. We evaluated the Ripple-Down Rules approach against the OIE systems, TEXTRUNNER and StatSnowball. In these studies the Ripple-Down Rules approach, with minimal low-cost rule addition achieves much higher precision and somewhat improved recall compared to these other Open Information Extraction systems.