Authors: Todorović, Branimir
Rančić, Svetozar
Marković, Ivica
Mulalić, Edin
Ilić, Velimir 
Title: Named entity recognition and classification using context hidden markov model
Journal: 9th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2008 Proceedings
First page: 43
Last page: 46
Conference: 9th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2008; Belgrade; Serbia; 25 September 2008 through 27 September 2008
Issue Date: 1-Dec-2008
ISBN: 978-142442904-2
DOI: 10.1109/NEUREL.2008.4685557
Abstract: 
Named entity (NE) recognition is a core technology for understanding low level semantics of texts. In this paper we report our preliminary results for Named Entity Recognition on MUC 7 corpus by combining the supervised machine learning system in the form of probabilistic generative Hidden Markov Model (HMM) for named entity classes PERSON, ORGANIZATION and LOCATION, and grammar based component for DATE, TIME, MONEY and PERCENT. We have implemented two variations of the basic Hidden Markov Model, where the second one is our version of HMM which uses the context of surrounding words to determine the NE class of the current word, leading to more accurate and faster NE recognition.
Keywords: Hidden Markov model | Information extraction | Named entity recognition | Viterbi decoding
Publisher: IEEE

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