[目的/意义] 提出一个全新的量化指标——文档主题新颖度，通过自然语言词对方法对文献主题内容的新颖性进行探测研究，并探讨其可行性和优缺点以及新颖度与F1000推荐文献和引文指标之间的关系。[方法/过程] 以F1000为基础，选取hematology主题近一个月内推荐的文献，在Pubmed中查找并获取该推荐文献发表之前6个月内密切相关的文献，构成整个文献集。定义自然语言法新颖度的概念、计算公式并利用Oracle数据库PL/SQL语言进行编程，通过MetaMap软件提取自然语言词汇进行文献主题新颖度的运算。[结果/结论] 自然语言法在文献主题新颖性探测的运算上具有一定的可行性；文档主题新颖度与F1000推荐文献、引用情况并非成等价关系，分属于科技论文评价的不同维度、不同范畴，不可一概而论。应将文档主题新颖度这一新指标与同行评议情况和文献计量学等其他相关论文评价指标结合起来对文献进行综合评价分析，选取优质文献给予推荐。
[Purpose/significance] This study proposes a new quantitative indicator:document theme novelty, through document theme novelty detection research with natural language pairs method, to discuss the feasibility, advantages and disadvantages as well as the novelty, and to explore its relationship among document theme novelty, F1000 recommend literature and citation index.[Method/process] Based on the F1000, this paper selected hematology theme literatures which were recommended nearly a month, then returned to Pubmed to search closely related literatures within six months before the publication of each recommended one to constitute the whole documents. The paper defined the concept of natural language theme novelty and calculation formula, used Oracle database with PL/SQL programming language, and extracted natural language word through MetaMap software for the calculation of the document theme novelty.[Result/conclusion] There is a certain feasibility in the novelty detection of literature theme operation of natural language method. Document theme novelty value, F1000 recommended literature, and citation index don't show the equivalence relation. They belong to different dimensions and different categories of scientific papers assessment, and cannot be treated as the same. It suggests that document theme novelty indicator should combine with peer review, literature metrology index, and other related thesis evaluation indexes for comprehensive evaluation of the literature analysis, to select high quality literature for recommendations.
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