@inproceedings{b9d28a30de0741d1903a56db7e1fa671,
title = "A semi-informative aware approach using topic model for medical search",
abstract = "We propose a semi-informative aware approach using the topic model on query expansion problem in the biomedicine domain. The demographics and disease information is applied to semi-structure the topic model as the 'known' label, compared to the traditional latent topics in topic modelling. Then, we suggest to select three terms from the top ranked documents to expand the query, based on the assumption in the pseudo relevance feedback method that the top ranked results in the first retrieval around are relevant. After that, we conduct the experiments on the TREC medical records data sets with extensive analysis and discussions. Numerically, we achieve the improvements of 7.41\% on MAP, 9.29\% on Bpref and 5.60\% on P@10 respectively over the strong baselines.",
author = "Hu, \{Qinmin Vivian\} and Liang He and Mingyao Li and Huang, \{Jimmy Xiangji\} and Haacke, \{E. Mark\}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014 ; Conference date: 02-11-2014 Through 05-11-2014",
year = "2014",
month = dec,
day = "29",
doi = "10.1109/BIBM.2014.6999177",
language = "英语",
series = "Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "320--324",
editor = "Huiru Zheng and Hu, \{Xiaohua Tony\} and Daniel Berrar and Yadong Wang and Werner Dubitzky and Jin-Kao Hao and Kwang-Hyun Cho and David Gilbert",
booktitle = "Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014",
address = "美国",
}