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@article{ART002697483},
author={Sukjin You},
title={The MeSH-Term Query Expansion Models using LDA Topic Models in Health Information Retrieval},
journal={Journal of Korean Library and Information Science Society},
issn={2466-2542},
year={2021},
volume={52},
number={1},
pages={79-108},
doi={10.16981/kliss.52.1.202103.79}

TY - JOUR
AU - Sukjin You
TI - The MeSH-Term Query Expansion Models using LDA Topic Models in Health Information Retrieval
JO - Journal of Korean Library and Information Science Society
PY - 2021
VL - 52
IS - 1
PB - Korean Library And Information Science Society
SP - 79
EP - 108
SN - 2466-2542
AB - Information retrieval in the health field has several challenges. Health information terminology is difficult for consumers (laypeople) to understand. Formulating a query with professional terms is not easy for consumers because health-related terms are more familiar to health professionals. If health terms related to a query are automatically added, it would help consumers to find relevant information. The proposed query expansion (QE) models show how to expand a query using MeSH terms. The documents were represented by MeSH terms (i.e. Bag-of-MeSH), found in the full-text articles. And then the MeSH terms were used to generate LDA (Latent Dirichlet Analysis) topic models. A query and the top k retrieved documents were used to find MeSH terms as topic words related to the query. LDA topic words were filtered by threshold values of topic probability (TP) and word probability (WP). Threshold values were effective in an LDA model with a specific number of topics to increase IR performance in terms of infAP (inferred Average Precision) and infNDCG (inferred Normalized Discounted Cumulative Gain), which are common IR metrics for large data collections with incomplete judgments. The top k words were chosen by the word score based on (TP *WP) and retrieved document ranking in an LDA model with specific thresholds. The QE model with specific thresholds for TP and WP showed improved mean infAP and infNDCG scores in an LDA model, comparing with the baseline result.
KW - MeSH;LDA;Topic Model;Information Retrieval;Query Expansion
DO - 10.16981/kliss.52.1.202103.79
ER -

Sukjin You. (2021). The MeSH-Term Query Expansion Models using LDA Topic Models in Health Information Retrieval. Journal of Korean Library and Information Science Society, 52(1), 79-108.

Sukjin You. 2021, "The MeSH-Term Query Expansion Models using LDA Topic Models in Health Information Retrieval", Journal of Korean Library and Information Science Society, vol.52, no.1 pp.79-108. Available from: doi:10.16981/kliss.52.1.202103.79

Sukjin You "The MeSH-Term Query Expansion Models using LDA Topic Models in Health Information Retrieval" Journal of Korean Library and Information Science Society 52.1 pp.79-108 (2021) : 79.

Sukjin You. The MeSH-Term Query Expansion Models using LDA Topic Models in Health Information Retrieval. 2021; 52(1), 79-108. Available from: doi:10.16981/kliss.52.1.202103.79

Sukjin You. "The MeSH-Term Query Expansion Models using LDA Topic Models in Health Information Retrieval" Journal of Korean Library and Information Science Society 52, no.1 (2021) : 79-108.doi: 10.16981/kliss.52.1.202103.79

Sukjin You. The MeSH-Term Query Expansion Models using LDA Topic Models in Health Information Retrieval. Journal of Korean Library and Information Science Society, 52(1), 79-108. doi: 10.16981/kliss.52.1.202103.79

Sukjin You. The MeSH-Term Query Expansion Models using LDA Topic Models in Health Information Retrieval. Journal of Korean Library and Information Science Society. 2021; 52(1) 79-108. doi: 10.16981/kliss.52.1.202103.79

Sukjin You. The MeSH-Term Query Expansion Models using LDA Topic Models in Health Information Retrieval. 2021; 52(1), 79-108. Available from: doi:10.16981/kliss.52.1.202103.79

Sukjin You. "The MeSH-Term Query Expansion Models using LDA Topic Models in Health Information Retrieval" Journal of Korean Library and Information Science Society 52, no.1 (2021) : 79-108.doi: 10.16981/kliss.52.1.202103.79