Predicting accuracy of extracting information from unstructured text collections

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 performance of real world information extraction tasks, Named Entity recognition and Relation Extraction. Categories and Subject Descriptors H.3.1 [INFORMATION STORAGE AND RETRIEVAL]...

• Type: Conference
• Year: 2005
• Where: CIKM
• Authors: Eugene Agichtein, Silviu Cucerzan
• Keywords: CIKM 2005, Information Extraction Tasks, Language Modeling, Relation Extraction