DocumentCode :
2915389
Title :
On a generalized name entity recognizer based on Hidden Markov Models
Author :
Colmenar, J. Manuel ; Abánades, Miguel A. ; Poza, Fernando ; Martín, Diego ; Cuesta, Alfredo ; Herràn, Alberto ; Hidalgo, J. Ignacio
Author_Institution :
CES Felipe II, Univ. Complutense de Madrid, Madrid, Spain
fYear :
2011
fDate :
22-24 Nov. 2011
Firstpage :
952
Lastpage :
958
Abstract :
This paper presents a Named Entity Recognition (NER) system based on Hidden Markov Models. The system design is language independent, and the target language and scope of the NER is determined by the training corpus. The NER is formed by two subsystems that detect and label the entities independently. Each subsystem implements a different approach of that statistical theory, showing that each component may complement the results of the other one. Unlike most of the previous works, two labels are returned when the components provide different results. This redundancy is an advantage when human supervision is mandatory at the end of the process such as in intelligence environments.
Keywords :
artificial intelligence; data mining; hidden Markov models; redundancy; text analysis; generalized name entity recognizer; hidden Markov models; human supervision; language independent system design; named entity recognition system; statistical theory; training corpus; Context; Hidden Markov models; Measurement; Nickel; Probability; Tagging; Training; Hidden Markov Models; Named Entity Recognition; Text Data Mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on
Conference_Location :
Cordoba
ISSN :
2164-7143
Print_ISBN :
978-1-4577-1676-8
Type :
conf
DOI :
10.1109/ISDA.2011.6121781
Filename :
6121781
Link To Document :
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