基于机器学习的古典诗词作者的判别研究

Studies of Traditional Chinese Poet Identification Based on Machine Learning

主要基于机器学习的NaveBaye等方法,对诗词文本采用向量空间模型来表示,首次提出了中国古代诗人李白和杜甫作品判别计算模型,并采用了信息增益作为特征选择的依据,结合爬山法完善了计算模型,该模型已经在精典语料《全唐诗》的机器学习基础上得到实现,获得较好的诗人作品判别效果,准确度可达到98.3%,该方法已成功地推广到古典诗词的风格流派的机器判别上[1],准确度达到88.5%。本文提出了古典诗词研究的技术方向。

Based on machine learning methods-Nave Baye, this paper proposes a Traditional Chinese Poetry anthor Identification Calculation to identify Libai or Dufu. That derive from Machine Learning Chinese Classical Poem in Tang dynasty. Vector space Model is the Knowledge representation of poetic text. Feature subset selection is peformed base on information gain and hill-climbling strategy. The 98.3% satifatictory accuracy rate is achieved. We also proposed Traditional Chinese Poetry Style Identification Modal with the same methods. The 88.5% style Identificaton accuracy rate is achieved. This paper proposes a text mining method of Traditional Chinese Poetry.