Document Similarity Search Based on Manifold-Ranking of TextTiles

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.