《Table 6 The performance of parent material value extraction using the conditional random fields bas

《Table 6 The performance of parent material value extraction using the conditional random fields bas   提示:宽带有限、当前游客访问压缩模式
本系列图表出处文件名:随高清版一同展现
《Automatic extraction and structuration of soil–environment relationship information from soil survey reports》


  1. 获取 高清版本忘记账户?点击这里登录
  1. 下载图表忘记账户?点击这里登录
1) The P,R and F1 denote separately precision,recall and F1-measure value.2) The Wn,Pn and Kn stand for word,part-of-speech,K value,and their context features in window sizes n.

To explore the impact of text types on the performance of the CRFs-based method,CRFs models of parent material were trained and validated separately by the descriptive texts of soil types and typical profiles.The validation results are shown in Tables 8 and 9.The maximum F1 value for the descriptive text of soil types was 90.7%,while the maximum F1 value for the descriptive text of soil profiles was only 75%.For the descriptive text of soil types,the optimal context window size was three and the optimal features were the combination of Wn,Pn and key value(Kn).For the descriptive text of soil profiles,the optimal context window size was two and the optimal features were the combination of Wn and Pn.These results indicated that text type had a significant impact on the performance of the CRFs model for the parent material variable.The descriptive texts of different soil types usually have similar styles and vocabularies to describe the parent material information,which are relatively easy for the CRFs to model.In contrast,the descriptive text of different soil profiles is often written using more diverse styles and vocabularies,so they are harder to be modeled.