Cluster-based query expansion using language modeling for biomedical literature retrieval

2021 ◽  
Author(s):  
Xuheng George Xu
Keyword(s):  
Query Expansion ◽  
Language Modeling ◽  
Biomedical Literature

scholarly journals An adaptive term proximity based rocchio’s model for clinical decision support retrieval

2019 ◽  
Vol 19 (S9) ◽  
Author(s):  
Min Pan ◽  
Yue Zhang ◽  
Qiang Zhu ◽  
Bo Sun ◽  
Tingting He ◽  
...  
Keyword(s):  
Query Expansion ◽  
Window Size ◽  
Clinical Decision ◽  
Recall Rate ◽  
Biomedical Literature ◽  
Query Term ◽  
Retrieval Models ◽  
Candidate Term ◽  
Adaptive Parameter ◽  
Clinical Support

Abstract Background In order to better help doctors make decision in the clinical setting, research is necessary to connect electronic health record (EHR) with the biomedical literature. Pseudo Relevance Feedback (PRF) is a kind of classical query modification technique that has shown to be effective in many retrieval models and thus suitable for handling terse language and clinical jargons in EHR. Previous work has introduced a set of constraints (axioms) of traditional PRF model. However, in the feedback document, the importance degree of candidate term and the co-occurrence relationship between a candidate term and a query term. Most methods do not consider both of these factors. Intuitively, terms that have higher co-occurrence degree with a query term are more likely to be related to the query topic. Methods In this paper, we incorporate original HAL model into the Rocchio’s model, and propose a new concept of term proximity feedback weight. A HAL-based Rocchio’s model in the query expansion, called HRoc, is proposed. Meanwhile, we design three normalization methods to better incorporate proximity information to query expansion. Finally, we introduce an adaptive parameter to replace the length of sliding window of HAL model, and it can select window size according to document length. Results Based on 2016 TREC Clinical Support medicine dataset, experimental results demonstrate that the proposed HRoc and HRoc_AP models superior to other advanced models, such as PRoc2 and TF-PRF methods on various evaluation metrics. Among them, compared with the Proc2 and TF-PRF models, the MAP of our model is increased by 8.5% and 12.24% respectively, while the F1 score of our model is increased by 7.86% and 9.88% respectively. Conclusions The proposed HRoc model can effectively enhance the precision and the recall rate of Information Retrieval and gets a more precise result than other models. Furthermore, after introducing self-adaptive parameter, the advanced HRoc_AP model uses less hyper-parameters than other models while enjoys an equivalent performance, which greatly improves the efficiency and applicability of the model and thus helps clinicians to retrieve clinical support document effectively.

An empirical study of query expansion and cluster-based retrieval in language modeling approach

2007 ◽  
Vol 43 (2) ◽  
pp. 302-314 ◽  
Author(s):  
Seung-Hoon Na ◽  
In-Su Kang ◽  
Ji-Eun Roh ◽  
Jong-Hyeok Lee
Keyword(s):  
Empirical Study ◽  
Query Expansion ◽  
Language Modeling ◽  
Modeling Approach

scholarly journals A Language Modeling Framework for Selective Query Expansion

2004 ◽  
Author(s):  
Steve Cronen-Townsend ◽  
Yun Zhou ◽  
W. B. Croft
Keyword(s):  
Query Expansion ◽  
Language Modeling ◽  
Modeling Framework

scholarly journals A hybrid query expansion framework for the optimal retrieval of the biomedical literature

Smart Health ◽  
2021 ◽  
pp. 100247
Author(s):  
Sumbal Malik ◽  
Umar Shoaib ◽  
Syed Ahmad Chan Bukhari ◽  
Hesham El Sayed ◽  
Manzoor Ahmed Khan
Keyword(s):  
Query Expansion ◽  
Biomedical Literature ◽  
Optimal Retrieval

The Center for International Biomedical Communications Research

1966 ◽  
Vol 05 (03) ◽  
pp. 142-146
Author(s):  
A. Kent ◽  
P. J. Vinken
Keyword(s):  
Information Needs ◽  
English Language ◽  
Medical Information ◽  
Biomedical Literature ◽  
Information Storage ◽  
Control Mechanisms ◽  
University Of Pittsburgh ◽  
The World ◽  
The University ◽  
Excerpta Medica Foundation

A joint center has been established by the University of Pittsburgh and the Excerpta Medica Foundation. The basic objective of the Center is to seek ways in which the health sciences community may achieve increasingly convenient and economical access to scientific findings. The research center will make use of facilities and resources of both participating institutions. Cooperating from the University of Pittsburgh will be the School of Medicine, the Computation and Data Processing Center, and the Knowledge Availability Systems (KAS) Center. The KAS Center is an interdisciplinary organization engaging in research, operations, and teaching in the information sciences.Excerpta Medica Foundation, which is the largest international medical abstracting service in the world, with offices in Amsterdam, New York, London, Milan, Tokyo and Buenos Aires, will draw on its permanent medical staff of 54 specialists in charge of the 35 abstracting journals and other reference works prepared and published by the Foundation, the 700 eminent clinicians and researchers represented on its International Editorial Boards, and the 6,000 physicians who participate in its abstracting programs throughout the world. Excerpta Medica will also make available to the Center its long experience in the field, as well as its extensive resources of medical information accumulated during the Foundation’s twenty years of existence. These consist of over 1,300,000 English-language _abstract of the world’s biomedical literature, indexes to its abstracting journals, and the microfilm library in which complete original texts of all the 3,000 primary biomedical journals, monitored by Excerpta Medica in Amsterdam are stored since 1960.The objectives of the program of the combined Center include: (1) establishing a firm base of user relevance data; (2) developing improved vocabulary control mechanisms; (3) developing means of determining confidence limits of vocabulary control mechanisms in terms of user relevance data; 4. developing and field testing of new or improved media for providing medical literature to users; 5. developing methods for determining the relationship between learning and relevance in medical information storage and retrieval systems’; and (6) exploring automatic methods for retrospective searching of the specialized indexes of Excerpta Medica.The priority projects to be undertaken by the Center are (1) the investigation of the information needs of medical scientists, and (2) the development of a highly detailed Master List of Biomedical Indexing Terms. Excerpta Medica has already been at work on the latter project for several years.

Query expansion based on global dependency parsing analysis

2013 ◽  
Author(s):  
Qinyuan Xiang ◽  
Weijiang Li ◽  
Hui Deng ◽  
Feng Wang
Keyword(s):  
Query Expansion ◽  
Dependency Parsing
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