Joel Nothman

See: Self-Supervised Named Entity Recognition, Wikipedia-based Named Entity Linking, Named Entity Linking.

References

(Hachey et al., 2013) ⇒ Ben Hachey, Will Radford, Joel Nothman, Matthew Honnibal, and James R. Curran. (2013). “Evaluating Entity Linking with Wikipedia.” In: Artificial Intelligence, 194.

(Nothman, 2009) ⇒ Joel Nothman, Tara Murphy, and James R. Curran. (2009). “Analysing Wikipedia and Gold-Standard Corpora for NER Training.” In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2009)
- http://portal.acm.org/citation.cfm?id=1609067.1609135
- ABSTRACT: Named entity recognition (ner) for English typically involves one of three gold standards: muc, conll, or bbn, all created by costly manual annotation. Recent work has used Wikipedia to automatically create a massive corpus of named entity annotated text.
  We present the first comprehensive cross-corpus evaluation of ner. We identify the causes of poor cross-corpus performance and demonstrate ways of making them more compatible. Using our process, we develop a Wikipedia corpus which outperforms gold standard corpora on cross-corpus evaluation by up to 11%.

(Nothman et al., 2008) ⇒ Joel Nothman, James R. Curran, and Tara Murphy. (2008). “Transforming Wikipedia into Named Entity Training Data.” In: Proceedings of the Australasian Language Technology Workshop.
(Nothman, 2008) ⇒ Joel Nothman. (2008). “Learning Named Entity Recognition from Wikipedia." Honours Thesis, School of Information Technologies, University of Sydney.

(Hughes et al., 2005) ⇒ Baden Hughes, James Haggerty, Joel Nothman, Saritha Manickam, and James R. Curran. (2005). “A Distributed Architecture for Interactive Parse Annotation.” In: Proceedings of the Australasian Language Technology Workshop 2005 (ALTW 2005)