A topic-based Document Correlation Model
| Autor: | Qi-Lun Zheng, Xi-Ping Jia, Zhao Li, Hong Peng, Zhuolin Jiang |
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| Rok vydání: | 2008 |
| Předmět: |
Vocabulary
Information retrieval Optimal matching business.industry Computer science media_common.quotation_subject Document clustering computer.software_genre Latent Dirichlet allocation Electronic mail symbols.namesake Text mining ComputingMethodologies_DOCUMENTANDTEXTPROCESSING Vector space model symbols Bipartite graph Data mining Document retrieval business computer Gibbs sampling media_common |
| Zdroj: | 2008 International Conference on Machine Learning and Cybernetics. |
| Popis: | Document correlation analysis is now a focus of study in text mining. This paper proposed a Document Correlation Model to capture the correlation between documents from topic level. The model represents the document correlation as the Optimal Matching of a bipartite graph, of which each partition is a document, each node is a topic, and each edge is the similarity between two topics. The topics of each document are retrieved by the Latent Dirichlet Allocation model and Gibbs sampling. Experiments on correlated document search show that the Document Correlation Model outperforms the Vector Space Model on two aspects: 1) it has higher average retrieval precision; 2) it needs less space to store a documentpsilas information. |
| Databáze: | OpenAIRE |
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