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Predicting Accuracy of Extracting Information from Unstructured Text Collections

Summary: Predicting Accuracy of Extracting Information from
Unstructured Text Collections
Eugene Agichtein Silviu Cucerzan
Microsoft Research
One Microsoft Way, Redmond, WA, USA
{eugeneag, silviu}@microsoft.com
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
extraction 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


Source: Agichtein, Eugene - Department of Mathematics and Computer Science, Emory University
Cucerzan, Silviu - Text Mining, Search, and Navigation Group, Microsoft Research


Collections: Computer Technologies and Information Sciences