【24h】

Emerging Topic Detection Model Based on LDA and Its Application in Stem Cell Field

机译:基于LDA的新兴主题检测模型及其在干细胞场中的应用

获取原文

摘要

Based on the investigation above background of stem cell research, this paper obtains the research topics of different time-window series with LDA topic segment model, and then the emerging topics are identified and judged according to the assumption of emerging topic definition. This paper proposes a new method to detect and identify the emerging topic in the topic evolution model. In this method, first, the time of the whole dataset is divided into several time-window series, and then the topics in total time-windows are segmented by LDA model. The composite relationships between topics are calculated by integrating the relationships of consistency, co-occurrence and semantics between topics. Those composite relationships are used to indicate and visualize the evolutionary relationships among topics. The emerging topics are detected by analyzing the characters of different evolutionary types including topics' differentiation, integration, emerging and decrease. And then the model's effectiveness is verified by case study in the stem cell field and expert judgment. Finally, the model's disadvantages and the next jobs are introduced in the paper.
机译:基于STEM Cell Research背景背景,本文获得了不同时间窗口系列与LDA主题段模型的研究主题,然后根据新兴主题定义的假设来识别并判断出现的主题。本文提出了一种在主题进化模型中检测和识别新兴主题的新方法。在此方法中,首先,整个数据集的时间被分成多个时间窗口系列,然后通过LDA模型分割总时间窗口中的主题。主题之间的复合关系是通过集成主题之间的一致性,共同发生和语义的关系来计算的。这些复合关系用于表示和可视化主题之间的进化关系。通过分析不同进化类型的特征来检测新出现的主题,包括主题分化,集成,新兴和减少。然后通过在干细胞领域和专家判断中核对模型的有效性。最后,纸质中介绍了该模型的缺点和下一份工作。

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号