Nymble System
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A Nymble System is a named entity recognition system that can find names and other non-recursive entities in a text item.
- AKA: Nymble.
- Context:
- It was first developed by Bikel et al. (1997).
- Example(s):
- the system described in Bikel et al. (1997),
- …
- Counter-Example(s):
- See: Hidden Markov Model, Natural Language Processing System, Named Entity, Message Understanding Conference, Image Recognizer.
References
1997
- (Bikel et al., 1997) ⇒ Daniel M. Bikel, Scott Miller, Richard M. Schwartz, and Ralph M. Weischedel. (1997). “Nymble: A High-Performance Learning Name-finder.” In: Proceedings of 5th Applied Natural Language Processing Conference (ANLP 1997).
- QUOTE: We have built a named-entity (NE) recognition system using a slightly-modified version of an HMM; we call our system “Nymble". To our knowledge, Nymble out-performs the best published results of any other learning name-finder. Furthermore, it performs at or above the 90% accuracy level, often considered "near-human performance".
The system arose from the NE task as specified in the last Message Understanding Conference (MUC), where organization names, person names, location names, times, dates, percentages and money amounts were to be delimited in text using SGML-markup.
- QUOTE: We have built a named-entity (NE) recognition system using a slightly-modified version of an HMM; we call our system “Nymble". To our knowledge, Nymble out-performs the best published results of any other learning name-finder. Furthermore, it performs at or above the 90% accuracy level, often considered "near-human performance".