KMi Seminars
ESpotter: A Domain and User Adaptation Approach for Named Entity Recognition on the Web
This event took place on Monday 14 June 2004 at 12:30

 
Jianhan Zhu

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. In this talk, I present a system called ESpotter, which improves NER on the Web by adapting lexicons and patterns to domains on the Web and user preferences. My results show that ESpotteqr provides more accurate and efficient NER on Web pages from various domains than current NER systems. ESpotter is implemented as a browser plug-in to help solve the information overload problem on the Web by discovering relevant information on user's behalf. Further work of integrating ESpotter with ontology based semantic browsing tool, Magpie, and the KMi semantic Web site are explored.

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KMi Seminars Event | SSSW 2013, The 10th Summer School on Ontology Engineering and the Semantic Web Journal | 25 years of knowledge acquisition
 

Social Software is...


Social Software
Social Software can be thought of as "software which extends, or derives added value from, human social behaviour - message boards, musical taste-sharing, photo-sharing, instant messaging, mailing lists, social networking."

Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.