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BioinQA: metadata-based multi-document QA system for addressing the issues in biomedical domain

机译:BioinQA:基于元数据的多文档质量检查系统,用于解决生物医学领域的问题

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摘要

Despite the availability of large amount of biomedical literature; extracting relevant information catering to the exact need of the user has been difficult in the absence of efficient domain specific information retrieval tools. Biomedical question answering (QA) systems require special techniques to address domain-specific issues, since a wide variety of user-groups having different information needs; terminology and level of understanding, etc., may access the information. While specialised information retrieval tools are not suitable for beginners, general purpose search engines are not intelligent enough to respond to domain specific questions. This paper presents an intelligent QA system that answers natural language questions while adapting itself to the level of user. The system constructs answers from multiple documents for complex comparison seeking questions. The system utilises metadata knowledge for addressing specific biomedical domain concerns like heterogeneity, acronyms, etc. Experiments performed show superiority of the system over popular commercial search engines such as Google, etc.
机译:尽管有大量的生物医学文献可供使用;在缺乏有效的领域特定信息检索工具的情况下,很难提取出满足用户确切需求的相关信息。生物医学问答系统(QA)需要特殊的技术来解决特定领域的问题,因为各种各样的用户组具有不同的信息需求。术语和理解水平等可以访问该信息。尽管专用的信息检索工具不适合初学者,但通用搜索引擎不够智能,无法回答特定领域的问题。本文提出了一种智能的QA系统,该系统可以回答自然语言的问题,同时使自己适应用户水平。该系统从多个文档构造答案,以进行复杂的比较查找问题。该系统利用元数据知识来解决特定的生物医学领域问题,例如异质性,首字母缩写词等。进行的实验表明,该系统优于流行的商业搜索引擎(例如Google)等。

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