Fully-Supervised Named Entity Recognition Algorithm

A Fully-Supervised Named Entity Recognition Algorithm is a Supervised NER Algorithm that is a Fully-Supervised Algorithm.

• AKA: Fully-Supervised NER Algorithm.
  • …
• Counter-Example(s):
  • a Semi-Supervised NER Algorithm.
  • a Dictionary-based NER Algorithm.
  • a Supervised Concept Mention Recognition Algorithm.
• See: Fully-Supervised WSD Algorithm.

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References

2007

• (Nadeau & Sekine, 2007) ⇒ David Nadeau, and Satoshi Sekine. (2007). “A Survey of Named Entity Recognition and Classification.” In: Lingvisticae Investigationes, 30(1).
  • The current dominant technique for addressing the NERC problem is supervised learning. SL techniques include Hidden Markov Models (HMM) (D. Bikel et al. 1997), Decision Trees (S. Sekine 1998), Maximum Entropy Models (ME) (A. Borthwick 1998), Support Vector Machines (SVM) (M. Asahara & Matsumoto 2003), and Conditional Random Fields (CRF) (A. McCallum & Li 2003).

2003

• (Bender et al., 2003) ⇒ Oliver Bender, Franz Josef Och, and Hermann Ney. (2003). “Maximum Entropy Models for Named Entity Recognition].” In: Proceedings of the seventh conference on Natural language learning HLT-NAACL 2003. doi:10.3115/1119176.1119196

2000

• (Baluja et al., 2000) ⇒ S. Baluja, V. Mittal, and R. Sukthankar. Applying Machine Learning for High Performance Named-Entity Extraction. Computational Intelligence, 16(4), November 2000.

1999

• (Bikel et al., 1999) ⇒ Daniel M. Bikel, Richard Schwartz, and Ralph M. Weischedel. (1999). “An Algorithm that Learns What‘s in a Name.” In: Machine Learning, 34. doi:10.1023/A:1007558221122

1998

• (Borthwick et al., 1998) ⇒ A. Borthwick, J. Sterling, Eugene Agichtein, and Ralph Grishman. (1998). “Exploiting Diverse Knowledge Sources via Maximum Entropy in Named Entity Recognition.” In: Proceedings of the 6th Workshop on Very Large Corpora.

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