摘要：

As most existing document retrieval models are inefficient in semantic learning and are unable to learn the document similarity in topic level, a topic-based document retrieval model (TDRM) is p TDRM provides a common topic space for all documents, represents each document as a vector in the common space, defines the document similarity as the cosine of the angle between document vectors, and uses Latent Dirichlet Allocation to learn the topic distribution of each document. Experimental results show that, as compared with the document similarity model based on the TextTiling and the optimal matching of bipartite graph, TDRM is of higher average precision and recall in the retrieval of similar document, with its harmonic mean of average precision and recall being 44% greater than that of the reference model.

展開

總結

以上是生活随笔為你收集整理的江计算机科学与技术,计算机科学与技术的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。