Document Similarity Search Based on Manifold-Ranking of TextTiles

  • Xiaojun Wan
  • Jianwu Yang
  • Jianguo Xiao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4182)


Document similarity search aims to find documents similar to a query document in a text corpus and return a ranked list of similar documents. Most existing approaches to document similarity search compute similarity scores between the query and the documents based on a retrieval function (e.g. Cosine) and then rank the documents by their similarity scores. In this paper, we proposed a novel retrieval approach based on manifold-ranking of TextTiles to re-rank the initially retrieved documents. The proposed approach can make full use of the intrinsic global manifold structure for the TextTiles of the documents in the re-ranking process. Experimental results demonstrate that the proposed approach can significantly improve the retrieval performances based on different retrieval functions. TextTile is validated to be a better unit than the whole document in the manifold-ranking process.


Ranking Score Retrieval Performance Vector Space Model Document Similarity Retrieval Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiaojun Wan
    • 1
  • Jianwu Yang
    • 1
  • Jianguo Xiao
    • 1
  1. 1.Institute of Computer Science and TechnologyPeking UniversityBeijingChina

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