Document Similarity Measure Based on Topic Model

Ming He; Zhen Zhen Wang; Yong Ping Du

doi:10.4028/www.scientific.net/AMM.513-517.1280

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 513-517Document Similarity Measure Based on Topic Model

Document Similarity Measure Based on Topic Model

Abstract:

Document similarity computation is an exciting research topic in information retrieval (IR) and it is a key issue for automatic document categorization, clustering analysis, fuzzy query and question answering. Topic model is an emerging field in natural language processing (NLP), IR and machine learning (ML). In this paper, we apply a latent Dirichlet allocation (LDA) topic model-based method to compute similarity between documents. By mapping a document with term space representation into a topic space, a distribution over topics derived for computing document similarity. An empirical study using real data set demonstrates the efficiency of our method.

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Periodical:

Applied Mechanics and Materials (Volumes 513-517)

Pages:

1280-1284

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.513-517.1280

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Online since:

February 2014

Authors:

Ming He*, Zhen Zhen Wang, Yong Ping Du

Keywords:

Document Similarity Computation, Latent Dirichlet Allocation, Topic Model

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