A B C D E F G H I J K L M N O P Q R S T U V W X Y Z all

espotter technology full details

external website icon

Champion: Enrico Motta
Professor of Knowledge Technologies Email Icon Website Icon RDF Icon

Participant(s):Jianhan Zhu, Victoria Uren

Timeline:22 Dec 2005

Share:

ESpotter

Adaptive Named Entity Recognition for Web Browsing

Named entity recognition (NER) systems are commonly designed with a "one-size-fits-all" philosophy. Lexicons and patterns manually crafted or learned from a training set of documents are applied to any other document without taking into account its background and user needs. However, when applying NER to Web pages, due to the diversity of these Web pages and user needs, one size frequently does not fit all. We present a system called ESpotter, which improves NER on the Web by adapting lexicons and patterns to domains on the Web and user preferences. Our results show that ESpotter provides more accurate and efficient NER on Web pages from various domains than current NER systems.

Publications

 

Zhu, J., Uren, V. and Motta, E. (2004) ESpotter: A Prototype System for Adaptive Named Entity Recognition Supporting Web Browsing, Whittlebury Hall, Northamptonshire, UK Proceedings of the 14th International Conference on Knowledge Engineering and Knowledge Management (EKAW'2004), Springer-Verlag

View By

Research Themes

Latest Seminar
Dr Nicole Lotz
The Open University

Creative engagement and co-design for inclusion

Watch the live webcast

CONTACT US

Knowledge Media Institute
The Open University
Walton Hall
Milton Keynes
MK7 6AA
United Kingdom

Tel: +44 (0)1908 653800

Fax: +44 (0)1908 653169

Email: KMi Support

COMMENT

If you have any comments, suggestions or general feedback regarding our website, please email us at the address below.

Email: KMi Development Team