期刊文献+

基于主题模型的法院文本典型案例推荐 被引量:3

Typical Case Recommendation of Court Texts Based on Topic Model
下载PDF
导出
摘要 考虑到案件不能量化、业务法官经验局限以及相似案件的认定缺乏统一的标准和尺度等因素,提出一种基于主题模型的法院文本典型案例推荐模型,基于本模型从相似度最大的文本中利用正则和加权的方法根据法院文本中判决年限和对应刑法给出推荐.同时对案件进行深度挖掘和分析,将相似度最高的案件定为最优推荐案件推荐给用户.实验结果表明,提出的基于主题模型的文本相似度计算方法跟传统的方法相比能够取得更好的F值. Taking into account that the case cannot be quantified, the limited experience of business judge, the lackof unified standards for the identification of similar cases and other factors, a typical model of court texts based ontopic model is proposed in this paper. The recommendation is given by regular and weighted method according to thenumber of years of judgment and corresponding criminal law from the maximum similarity text in court texts basedin this model. While the highest similarity of the case as the best recommendation case to the user. Experimentalresults show that the method based on topic model proposed in this paper is superior to the traditional text similaritymeasure in F-metrics.
作者 吕宾 侯伟亮
出处 《微电子学与计算机》 CSCD 北大核心 2018年第2期128-132,共5页 Microelectronics & Computer
基金 国家重点研发计划(2016YFC0800802)
关键词 主题模型LDA 多粒度文本特征提取 文本相似度 法院判决文本 topic model LI)A multi-granularity text feature extraction text similarity court texts
  • 相关文献

参考文献4

二级参考文献49

  • 1张敏,耿焕同,王煦法.一种利用BC方法的关键词自动提取算法研究[J].小型微型计算机系统,2007,28(1):189-192. 被引量:19
  • 2穗志文.基于骨架依存树的语句相似度计算模型[J].计算语言学文集,1998,(3):176-184.
  • 3Fung B C M,Wang K,Ester M.Hierarchical document clustering//Wang John ed.The Encyclopedia of Data Warehousing and Mining,idea Group.2005:970-975.
  • 4Salton G.The SMART Retrieval System-Experiments in Automatic Document Processing.Englewood Cliffs,New Jersey:Prentice Hall Inc,1971.
  • 5Wang Y,Julia H.Document clustering with semantic analysis//Proceedings of the 39th Hawaii International Conferences on System Sciences.Hawaii,US,2006:54-63.
  • 6Hotho A,Staab S,Stumme G.Wordnet improves text document clustering//Proceedings of the Semantic Web Workshop at SIGIR-2003,26th Annual International ACM SIGIR Conference.Toronto,Canada,2003:541-550.
  • 7Hall P,Dowling G.Approximate string matching.Computing Survey,1980,12(4):381-402.
  • 8Coelho T,Calado P,Souza L,Ribeiro-Neto B,Muntz R.Image retrieval using multiple evidence ranking.IEEETransactions on Knowledge and Data Engineering,2004,16(4):408-417.
  • 9Ko Y,Park J,Seo J.Improving text categorization using the importance of sentences.lnformation Processing and Management,2004,40(1):65-79.
  • 10Erkan G,Radev D.Lexrank:Graph-based lexical centrality as salience in text summarization.Journal of Artificial Intelligence Research,2004,22(7):457-479.

共引文献329

同被引文献35

引证文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部