Improving biomedical information retrieval by linear combinations of different query expansion techniques
Improving biomedical information retrieval by linear combinations of different query expansion techniques
复制标题
DOI：
10.1186/s12859-016-1092-8
复制
发表时间：
2016-07-25
影响因子：
3
通讯作者：
Banbhrani, Santosh Kumar
中科院分区：
文献类型：
--
作者：
Abdulla, Ahmed AbdoAziz Ahmed;Lin, Hongfei;Banbhrani, Santosh Kumar
背景：生物医学文献检索变得越来越复杂，迫切需要先进的信息检索系统。信息检索（IR）程序搜索非结构化材料，例如通常存储在计算机上的大量数据中的文本文档。 IR 与信息项的表示、存储和组织以及访问有关。在 IR 中，主要问题之一是确定哪些文档相关，哪些文档不符合用户的需求。在当前制度下，用户无法以准确的方式精确地构建查询以从大量数据中检索特定的数据片段。基本信息检索系统正在产生低质量的搜索结果。在本文提出的系统中，我们提出了一种改进信息检索搜索的新技术，以更好地表示用户的信息需求，以便通过使用不同的查询扩展技术并在它们之间应用线性组合来增强信息检索的性能，其中组合一次在两个扩展结果之间呈线性。查询扩展扩展了搜索查询，例如，通过查找同义词和重新加权原始术语。与基本搜索查询相比，它们提供了明显更集中、更具体的搜索结果。结果：检索性能是通过 MAP（平均平均精度）的一些变体来衡量的，根据我们的实验结果，查询扩展的最佳结果的组合增强了检索到的文档，并且比我们的基线提高了 21.06%，甚至比之前的研究提高了 7.12%。结论：我们提出了几种查询扩展技术及其组合（线性），以使用户查询更多可以被搜索引擎识别并产生更高质量的搜索结果。
Background: Biomedical 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.Results: 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 %.Conclusions: We 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.