scholarly journals Query Expansion Using Medical Information Extraction for Improving Information Retrieval in French Medical Domain

2018 ◽  
Vol 14 (3) ◽  
pp. 1-17 ◽  
Author(s):  
Aicha Ghoulam ◽  
Fatiha Barigou ◽  
Ghalem Belalem ◽  
Farid Meziane
Keyword(s):  
Information Retrieval ◽  
Information Needs ◽  
Query Expansion ◽  
Medical Information ◽  
Expansion Method ◽  
Relevant Information ◽  
Medical Texts ◽  
Named Entities ◽  
Medical Information Retrieval ◽  
Large Container

This article describes how many users' queries contain references to named entities, and this is particularly true in the medical field. Doctors express their information needs using medical entities as they are element rich with information that helps better target relevant documents. At the same time, many resources have been recognized as a large container of medical entities and relationships between them such as clinical reports; which are medical texts written by doctors. In this article, the authors present a query expansion method that uses medical entities and their semantic relations in the query context based on an external resource in OWL. The goal of this method is to evaluate the effectiveness of an information retrieval system to support doctors in accessing easily relevant information. Experiments on a collection of real clinical reports show that their approach reveals interesting improvements in precision, recall and MAP in medical information retrieval.

Literature Review in Computational Linguistics Issues in the Developing Field of Consumer Informatics

2011 ◽  
pp. 226-232
Author(s):  
Ki Jung Lee
Keyword(s):  
Information Retrieval ◽  
Computational Linguistics ◽  
Information Structure ◽  
Information Needs ◽  
Retrieval System ◽  
Medical Information ◽  
Information Retrieval System ◽  
Search Results ◽  
Medical Information Retrieval ◽  
On Line

With the increased use of Internet, a large number of consumers first consult on line resources for their healthcare decisions. The problem of the existing information structure primarily lies in the fact that the vocabulary used in consumer queries is intrinsically different from the vocabulary represented in medical literature. Consequently, the medical information retrieval often provides poor search results. Since consumers make medical decisions based on the search results, building an effective information retrieval system becomes an essential issue. By reviewing the foundational concepts and application components of medical information retrieval, this paper will contribute to a body of research that seeks appropriate answers to a question like “How can we design a medical information retrieval system that can satisfy consumer’s information needs?”

Literature Review in Computational Linguistics Issues in the Developing Field of Consumer Informatics

2010 ◽  
pp. 758-765 ◽  
Author(s):  
Ki Jung Lee
Keyword(s):  
Information Retrieval ◽  
Computational Linguistics ◽  
Information Structure ◽  
Information Needs ◽  
Retrieval System ◽  
Medical Information ◽  
Information Retrieval System ◽  
Search Results ◽  
Medical Information Retrieval ◽  
On Line

With the increased use of Internet, a large number of consumers first consult on line resources for their healthcare decisions. The problem of the existing information structure primarily lies in the fact that the vocabulary used in consumer queries is intrinsically different from the vocabulary represented in medical literature. Consequently, the medical information retrieval often provides poor search results. Since consumers make medical decisions based on the search results, building an effective information retrieval system becomes an essential issue. By reviewing the foundational concepts and application components of medical information retrieval, this paper will contribute to a body of research that seeks appropriate answers to a question like “How can we design a medical information retrieval system that can satisfy consumer’s information needs?”

A Novel Hybrid Correlation Measure for Query Expansion-Based Information Retrieval

2020 ◽  
pp. 1-19 ◽  
Author(s):  
Ilyes Khennak ◽  
Habiba Drias
Keyword(s):  
Information Retrieval ◽  
Information Needs ◽  
Query Expansion ◽  
Relevant Information ◽  
Medical Database ◽  
Correlation Measure ◽  
Real Dataset ◽  
Correlation Measures ◽  
Degree Of Similarity ◽  
Prior State

Query expansion (QE) is one of the most effective techniques to enhance the retrieval performance and to retrieve more relevant information. It attempts to build more useful queries by enriching the original queries with additional expansion terms that best characterize the users' information needs. In this chapter, the authors propose a new correlation measure for query expansion to evaluate the degree of similarity between the expansion term candidates and the original query terms. The proposed correlation measure is a hybrid of two correlation measures. The first one is considered as an external correlation and it is based on the term co-occurrence, and the second one is considered as an internal correlation and it is based on the term proximity. Extensive experiments have been performed on MEDLINE, a real dataset from a large online medical database. The results show the effectiveness of the proposed approach compared to prior state-of-the-art approaches.

A method of query expansion based on topic models and user profile for search in folksonomy

2021 ◽  
pp. 1-11
Author(s):  
Zhinan Gou ◽  
Yan Li
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Information Overload ◽  
Topic Model ◽  
User Profile ◽  
Expansion Method ◽  
Collaborative Tagging ◽  
Search Query ◽  
Tagging System ◽  
The Web

With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

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