Deep Level Markov Chain Model for Semantic Document Retrieval

Authors

DOI:

https://doi.org/10.4108/eai.19-6-2018.155443

Keywords:

Big data, information retrieval, feature reduction, Markov chain, probability inference

Abstract

The task of researching and developing information retrieval systems is becoming important in the big data age. Current search methods try to mention to fast searching based on keyword matching or similar semantic between query and documents but have not got a really effective engine for semantic search . In this paper, we propose a method for information retrieval based on probability inference with the DLMC model to search by semantic equivalents and a topic word with score for fast searching. Results of the experimental with 952 Vietnamese documents show that our method is really effective for Vietnamese document retrieval system.

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Published

10-09-2018

How to Cite

1.
Khanh LB, Nguyen Thi Thu H, Dao Thanh T. Deep Level Markov Chain Model for Semantic Document Retrieval. EAI Endorsed Scal Inf Syst [Internet]. 2018 Sep. 10 [cited 2024 Nov. 14];5(19):e1. Available from: https://publications.eai.eu/index.php/sis/article/view/2184