JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2010, Vol. 40 ›› Issue (5): 141-145.

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Domain of automatic entity relation extraction based on seed self-expansion and maximum entropy machine learning

LEI Chun-ya1, GUO Jian-yi1,2, YU Zheng-tao1,2, MAO Cun-li1,2, ZHANG Shao-min1, HUANG Pu1   

  1. 1. School of Information Engineering and Automation, Kunming University of Science and Technology,
    Kunming 650051, China;  2. Institute of Intelligent Information Processing, Computer Technology Application Key
     Laboratory of  Yunnan Province, Kunming 650051, China
  • Received:2010-03-15 Online:2010-10-16 Published:2010-03-15

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Abstract:

Entity relation extraction is one of difficulties in information extraction’s field.In this paper, a method that seed selfexpansion and maximum entropy machine learning was proposed to extract entity relation in the filed of tourism. Firstly, used seed self-expansion to get words semantic that express the big types relation between entity pairs, and this words semantic as a characteristic was added to the set of characteristics, meanwhile designed threshold to tag studying corpus automatically; then used maximum entropy machine learning algorithm to learn corpus tagged and built the classifier of entity relation extraction. Experiments based on artificial collection of 600 corpuses obtained a better result for four big types of entity relation extraction, the F values reached 82.56％ and 81.17％ in which the two big types relation of geographical location and date-season, it showed in the condition of less manual participation, adding the word semantic of entity pairs could effectively improve the performance of classifier.

Key words: entity relation extraction, maximum entropy, self-expansion, feature

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