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Applying Lemur Query Expansion Techniques in Biomedical Information Retrieval

机译：狐猴查询扩展技术在生物医学信息检索中的应用

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摘要

The increase in the amount of available biomedical information has resulted in a higher demand on biomedical information retrieval systems. However, traditional information retrieval systems do not achieve the desired performance in this area. Query expansion techniques have improved the effectiveness of ranked retrieval by automatically adding additional terms to a query. In this work we test several automatic query expansion techniques using the Lemur Language Modelling Toolkit. The objective is to evaluate a set of query expansion techniques when they are applied to biomedical information retrieval. In the first step of the information retrieval searching, indexing, we compare the use of several techniques of stemming and stopwords. In the second step, matching, we compare the well-known weighting algorithms Okapi and TF-IDF BM25. The best results are obtained with the combination of Krovetz stemmer, SMART stopword list and TF-IDF. Moreover, we analyze the document retrieval based on Abstract, Title and Mesh fields. We conclude that seems more effective than looking at each of these fields individually. Also, we show that the use of feedback in document retrieval results a improvement in retrieving. The corpus used in the experiments was extracted from the biomedical text Cystic Fibrosis Corpus (CF).
机译：可获得的生物医学信息量的增加导致对生物医学信息检索系统的更高需求。但是，传统的信息检索系统在该领域无法实现所需的性能。通过自动向查询中添加其他术语，查询扩展技术提高了排名检索的效率。在这项工作中，我们使用Lemur语言建模工具包测试了几种自动查询扩展技术。目的是评估将一组查询扩展技术应用于生物医学信息检索时的技术。在信息检索搜索，索引编制的第一步中，我们比较了词干和停用词的几种技术的使用。在第二步匹配中，我们比较了众所周知的加权算法Okapi和TF-IDF BM25。结合Krovetz词干提取器，SMART停用词列表和TF-IDF可获得最佳结果。此外，我们分析了基于“摘要”，“标题”和“网格”字段的文档检索。我们得出结论，这似乎比单独查看每个领域都更有效。此外，我们还表明在文档检索中使用反馈会导致检索方面的改进。实验中使用的语料库是从生物医学文献Cystic Fibrosis Corpus（CF）中提取的。

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