计算机科学 ›› 2021, Vol. 48 ›› Issue (11): 170-175.doi: 10.11896/jsjkx.201100004
所属专题: 大数据&数据科学 虚拟专题
匡广生1,2, 郭岩2, 俞晓明2, 刘悦2, 程学旗2
KUANG Guang-sheng1,2, GUO Yan2, YU Xiao-ming2, LIU Yue2, CHENG Xue-qi2
摘要: 在给定的任务中分析各种数据时,目前大多数研究只针对单源数据进行分析,缺乏应用于多源数据的方法。但如今数据日益丰富,因此提出一种多源数据融合框架,用于融合多种网络平台数据。同一平台数据中包含文本与各种属性,同时不同平台的数据在内容与形式方面也存在很大差异。然而现有的网络信息挖掘方法大多仅使用同一平台中的部分数据进行分析,忽略了不同平台的数据之间存在的相互作用。因此文中提出一种数据融合框架,一方面,能基于图的强大表示能力融合同一平台不同类型的特征,从而提升单个平台的任务性能;另一方面能够利用不同平台的数据特征,使其相互补充,从而提升多个平台的任务性能。文中讨论的融合数据类型包括文本、时间、作者信息,这些特征涉及连续特征、离散特征以及非结构化特征。所提框架在事件分类任务上提升了F1值,验证了提出的多源数据框架的有效性。
中图分类号:
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