Predicting accuracy of extracting information from unstructured text collections

Eugene Agichtein, Silviu Cucerzan
2005 Proceedings of the 14th ACM international conference on Information and knowledge management - CIKM '05  
Exploiting lexical and semantic relationships in large unstructured text collections can significantly enhance managing, integrating, and querying information locked in unstructured text. Most notably, named entities and relations between entities are crucial for effective question answering and other information retrieval and knowledge management tasks. Unfortunately, the success in extracting these relationships can vary for different domains, languages, and document collections. Predicting
more » ... traction performance is an important step towards scalable and intelligent knowledge management, information retrieval and information integration. We present a general language modeling method for quantifying the difficulty of information extraction tasks. We demonstrate the viability of our approach by predicting performance of real world information extraction tasks, Named Entity recognition and Relation Extraction.
doi:10.1145/1099554.1099678 dblp:conf/cikm/AgichteinC05 fatcat:ibof63i6ovezpnp4n5sqw3joo4