计算机科学 ›› 2010, Vol. 37 ›› Issue (2): 189-191.

• 人工智能 • 上一篇    下一篇

术语关系自动抽取方法研究

孙霞,王小凤,董乐红,吴江   

  1. (西北大学信息技术与科技学院计算机系 西安710127)
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受陕四省教育厅项目(09JK768,09JK774,09JK738)资助。

Study on Term Relation Extraction from Domain Text

SUN Xia,WANG Xiao-feng,DONG Le-hong,WU Jiang   

  • Online:2018-12-01 Published:2018-12-01

摘要: 将术语关系抽取转化为分类问题,给出了基于机器学习的术语关系自动抽取流程。针对现有产生式和判定学习算法的缺点,提出了混合分类算法HC。该算法使得一部分特征值通过训练数据佑计而来,另一部分特征值通过判定函数训练得到。实验结果表明,该算法优于原来的产生式学习算法和判断学习算法,在人工标注的小训练集上获得了较好的分类效果。

关键词: 机器学习,术语关系抽取,混合学习算法

Abstract: A term relation extraction approach was proposed. It was cast as a classification task. The hybrid classification algorithm combining the advantages of both naive bayes and perceptron was also presented. In this algorithm, a subset of the features was estimated from training data, and another subset of the features was trained by discriminative function. The experimental results showed that the proposed hybrid algorithm almost always outperforms the naive bayes algorithms and perceptron algorithms when the training set is small.

Key words: Machine learning,Term relation extraction,Classification algorithm

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