Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Named entity recognition through classifier combination
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
A stacked, voted, stacked model for named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Boosting performance of bio-entity recognition by combining results from multiple systems
Proceedings of the 5th international workshop on Bioinformatics
Combining data-driven systems for improving Named Entity Recognition
Data & Knowledge Engineering
Foundations and Trends in Databases
NERD: a framework for unifying named entity recognition and disambiguation extraction tools
EACL '12 Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics
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Huge amounts of textual information relevant for market analysis, trending or product monitoring can be found on the Web. To exploit that knowledge a number of extraction services were proposed that extract and categorize entities from given text. Prior work showed that a combination of individual extractors can increase quality. However, so far no system exists that is fully applicable to reasonably combine real world extraction services that differ substantially in the entity types they extract and the schemata used. In this paper, we propose an aggregation system and a corresponding aggregation process that can be used for these services. We present a number of novel aggregation techniques that incorporate schema-information as well as entity extraction specific characteristics into the aggregation process. The aggregation system is broadly evaluated on six real world named entity recognition services and compared to state of the art approaches.