Research Article
Deep Level Markov Chain Model for Semantic Document Retrieval
@ARTICLE{10.4108/eai.19-6-2018.155443, author={Linh Bui Khanh and Ha Nguyen Thi Thu and Tinh Dao Thanh}, title={Deep Level Markov Chain Model for Semantic Document Retrieval}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={5}, number={19}, publisher={EAI}, journal_a={SIS}, year={2018}, month={9}, keywords={Big data, information retrieval, feature reduction, Markov chain, probability inference}, doi={10.4108/eai.19-6-2018.155443} }- Linh Bui Khanh
Ha Nguyen Thi Thu
Tinh Dao Thanh
Year: 2018
Deep Level Markov Chain Model for Semantic Document Retrieval
SIS
EAI
DOI: 10.4108/eai.19-6-2018.155443
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.
Copyright © 2018 Linh Bui Khanh et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
