Algorithm of Beijing Opera Organization Names Entity Recognition Based on HMM
Xi Zhao · Jisuanji gongcheng · 2013
Aiming at the inefficiency of organization named entity recognition,this paper proposes an algorithm of Beijing opera organization Named Entity Recognition(NER) based on Hidden Markov Model(HMM).It uses HMM to take part-of-speech tagging and solve the problem of disambiguation of the words.The Viterbi algorithm is used to calculate the maximum probability tagging sequence to the sentence segmentation.It defines the rules to recognize the organization names.The left and right boundary of the organization is identified with the help of organization postfix lexicon.The new names in corpus are recognized by automatic algorithm and be loaded into the dictionary.This paper takes the test in open materials,the result shows the recognition accuracy can achieve 99%.