Incorporating global information into named entity recognition systems using relational context

Authors:
Yuval Merhav;Filipe Mesquita;Denilson Barbosa;Wai Gen Yee;Ophir Frieder
Affiliations:
Illinois Institute of Technology, Chicago, IL, USA;University of Alberta, Edmonton, AB, Canada;University of Alberta, Edmonton, AB, Canada;Illinois Institute of Technology, Chicago, IL, USA;Georgetown University, Washington, DC, USA
Venue:
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Year:
2010

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Abstract

The state-of-the-art in Named Entity Recognition relies on a combination of local features of the text and global knowledge to determine the types of the recognized entities. This is problematic in some cases, resulting in entities being classified as belonging to the wrong type. We show that using global information about the corpus improves the accuracy of type identification. We explore the notion of a global domain frequency that relates relation identifying terms with pairs of entity types which are used in that relation. We use this to identify entities whose types are not compatible with the terms they co-occur in the text. Our results on a large corpus of social media content allows the identification of mistyped entities with 70% accuracy.