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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.


Jianhan Zhu Victoria Uren Enrico Motta


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