Abstract
BackgroundBiomedical literature retrieval is becoming increasingly complex, and there is a fundamental need for advanced information retrieval systems. Information Retrieval (IR) programs scour unstructured materials such as text documents in large reserves of data that are usually stored on computers. IR is related to the representation, storage, and organization of information items, as well as to access. In IR one of the main problems is to determine which documents are relevant and which are not to the user’s needs. Under the current regime, users cannot precisely construct queries in an accurate way to retrieve particular pieces of data from large reserves of data. Basic information retrieval systems are producing low-quality search results. In our proposed system for this paper we present a new technique to refine Information Retrieval searches to better represent the user’s information need in order to enhance the performance of information retrieval by using different query expansion techniques and apply a linear combinations between them, where the combinations was linearly between two expansion results at one time. Query expansions expand the search query, for example, by finding synonyms and reweighting original terms. They provide significantly more focused, particularized search results than do basic search queries.ResultsThe retrieval performance is measured by some variants of MAP (Mean Average Precision) and according to our experimental results, the combination of best results of query expansion is enhanced the retrieved documents and outperforms our baseline by 21.06 %, even it outperforms a previous study by 7.12 %.ConclusionsWe propose several query expansion techniques and their combinations (linearly) to make user queries more cognizable to search engines and to produce higher-quality search results.
Highlights
Biomedical literature retrieval is becoming increasingly complex, and there is a fundamental need for advanced information retrieval systems
In [6], Lv et al, published a study about how to select effectively from feedback documents words that are more related to the query topic based on positions of terms in feedback documents. They used a positional relevance model (PRM) to address this problem in a unified, probabilistic way. The results of their experiment on two large web data sets show that the proposed PRM is quite effective and robust and performs significantly better than state-of-theart relevance model in both document-based feedback and passage-based feedback
After comparison we found that the combination between Feedback and PubMed Expansion outperformed the baseline by 21.065 %, and outperformed previous study [5] by 7.12 %
Summary
The retrieval performance is measured by some variants of MAP (Mean Average Precision) and according to our experimental results, the combination of best results of query expansion is enhanced the retrieved documents and outperforms our baseline by 21.06 %, even it outperforms a previous study by 7.12 %
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