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Section-wise indexing and retrieval of research articles

机译:章节索引和检索研究文章

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摘要

Relevant information extraction is a dire need of the scholarly community. There are a number of systems available to find relevant information from scientific literature such as search engines, citation indexes, digital libraries etc. For a search query, a long list of irrelevant documents is presented to the users mainly due to the huge number of availability of the full-text document, and furthermore due to the unstructured nature of indexed scientific resources. The contemporary systems have formally defined the structure of scientific documents. However, populating the already available enriched scientific structure from unstructured/semi-structured scientific documents has not been addressed previously. In this research paper, we have designed, implemented, and evaluated an automated technique that is able to tag each paper's content with logical sections appearing in the scientific document. The proposed system has been evaluated against the benchmark, subsequently, the proposed system have been also compared with machine learning techniques that may be used for the same task. It has been empirically shown that the overall correctness and completeness of our proposed technique is 0.78 and 0.79 respectively and thus the overall accuracy of about 0.78 was achieved. The achieved results are good as compared to machine learning based classification. The developed system may help future information retrieval systems, digital libraries, and citation indexes to index, retrieve, rank and visualize most relevant scientific documents for the scientific community.
机译:相关信息提取是学术界的可怕需求。有许多系统可用于从科学文献中查找相关信息,如搜索引擎,引文索引,数字图书馆等。对于搜索查询,将呈现给用户的长期无关文档,主要原因是由于可用性大量全文文件,而且由于索引科学资源的非结构化性质。当代系统已正式定义了科学文件的结构。但是,此前填充了来自非结构化/半结构化科学文件的已有丰富的科学结构尚未得到解决。在本研究论文中,我们设计了设计,实施和评估了能够用科学文档中出现的逻辑部分标记每个纸张的内容。该提出的系统已经针对基准进行了评估,随后,已将所提出的系统与可用于相同任务的机器学习技术进行比较。经验证明,我们所提出的技术的总体正确性和完整性分别为0.78和0.79,因此实现了约0.78的整体精度。与基于机器学习的分类相比,实现的结果很好。开发系统可以帮助未来的信息检索系统,数字图书馆和引文指标来指定,检索,等级和可视化科学界的大多数相关的科学文档。

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