Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
A maximum entropy approach to information extraction from semi-structured and free text
Eighteenth national conference on Artificial intelligence
Kernel methods for relation extraction
The Journal of Machine Learning Research
Message Understanding Conference-6: a brief history
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Complexity of event structure in IE scenarios
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Overview of results of the MUC-6 evaluation
MUC6 '95 Proceedings of the 6th conference on Message understanding
Description of the UMass system as used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
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Some Information Extraction (IE) systems are limited to extracting events expressed in a single sentence. It is not clear what effect this has on the difficulty of the extraction task. This paper addresses the problem by comparing a corpus which has been annotated using two separate schemes: one which lists all events described in the text and another listing only those expressed within a single sentence. It was found that only 40.6% of the events in the first annotation scheme were fully contained in the second.