Nowadays, one subfield of information extraction, Named Entity Recognition, becomes more and more important. It helps machine to recognize proper nouns (entities) in text and associating them with the appropriate types. Common types in NER systems are location, person name, date, address, etc. There are several NER systems in the world. What‘s the main core technology of these systems? Which kind of system is better? How to improve this technology in the future? This master thesis will show the basic and detail knowledge about NER.Three existing NER systems will be choose to evaluate in this paper, GATE, CRFClassifier and LbjNerTagger. These systems are based different NER technology. They can stand for the most of NER existing systems in the world now. This paper will present and evaluate these three systems and try to find the advantage and disadvantage of each system.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ntnu-11223 |
| Date | January 2010 |
| Creators | Sun, Bowen |
| Publisher | Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, Institutt for datateknikk og informasjonsvitenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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