Named Entity Recognition and Classification using Context Hidden Markov Model

被引：0
|
作者：
Todorovic, Branimir T. [1 ]
Rancic, Svetozar R. [1 ]
Markovic, Ivica M. [2 ]
Mulalic, Edin H. [3 ]
Ilic, Velimir M. [3 ,4 ]
机构：
[1] Univ Nis, Dept Math & Informat, Fac Sci & Math, Nish, Serbia
[2] Univ Nis, Fac Elect, Dept Comp Sci, Nish, Serbia
[3] Accordia Grp LLC, Nish, Serbia
[4] Univ Nis, Fac Elect, Dept Telecommun, Nish, Serbia
来源：
NEUREL 2008: NINTH SYMPOSIUM ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING, PROCEEDINGS | 2008年
关键词：
Information extraction; named entity recognition; hidden Markov model; Viterbi decoding;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：41 / +
页数：2
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