基于命名实体敏感的分层新闻故事线生成方法

社会网络中海量、无序且碎片化的新闻数据,使得人们无法从细粒度感知新闻事件,更无法多视角把握事件发展脉络。为了解决这个问题,该文提出基于命名实体敏感的分层新闻故事线生成方法,在无监督的情况下,充分利用新闻信息构造层次化、多视点的事件脉络。该方法主要通过以下3个步骤实现:(1)基于事件主题信息与隐式语义信息相结合的方法检测事件;(2)基于多维语义信息的社区检测算法划分主题事件的子事件;(3)基于多视点信息构造事件发展的脉络。在真实数据集上的实验结果表明,该方法在三个步骤比基线方法均有提高,其中在构造事件发展脉络阶段,该方法在理解性、概括性和准确性指标上分别高出0.44、0.11和0.50。

Named Entity Sensitive Generation of Hierarchical News Storyline

The massive, disorderly and fragmented news data in the social network makes it impossible for people to perceive news event details from a multi-dimensional perspective. To address this issue, this paper proposes a named entity sensitive generation of hierarchical news story line, so as to form a hierarchical and multi-view event context development without supervision. Firstly, the event is detected based on the combination of event topic information and implicit semantic information; Then the community detection algorithm based on multi-dimensional semantics is applied to divide the event into multiple clusters, with each cluster as a sub-event. Finally the event storyline is constructed from the multi-view of information. Experimental results on real-world dataset demonstrate that the proposed method outperforms the baseline method in each step, with increases in terms of acceptability , generality and correctness by of 0.44, 0.11 and 0.50, respectively.