《马太福音》、《马可福音》、《路加福音》、《约翰福音》和《使徒行传》中圣经人名的关系
Biblical names' relationships in the Gospel of Matthew, Mark, Luke, John and Acts of Apostles
摘要：
在本文中，我们通过使用基于文本挖掘网络的方法来推断有关圣经人物和地点及其相互关系的信息。我们研究了五本书的WEB版本的叙事结构：马太福音，马可福音，路加福音，约翰福音和使徒行传。主要的重点是主角的名字的相互关系，在分析的方式，即使用各种网络为基础的方法和描述符。这个语料库是为了创建一个网络而处理的：我们从维基百科的圣经名字列表中下载人名和地名，然后我们在每节经文中寻找它们的同现，在这个过程的最后，我们得到N个同现的名字。两个名字之间的联系的强度被定义为这些名字在所有经文中一起出现的次数的总和，这样我们就得到了N乘N对名字的5个邻接矩阵（每本书一个）。在这个预处理阶段之后，对于5本分析过的书中的每一本，我们计算主要的网络中心性度量（经典度、加权度、介数和接近度）、网络脆弱性，并运行社区发现算法来突出弥赛亚在整个网络和他的群体（社区）中的作用。我们发现，所提出的方法是适合突出的名称同现的结构。所发现的框架结构有助于从结构学的角度解释人物的情节。
英文摘要：
In this paper we extrapolate the information about Bible's characters and places, and their interrelationships, by using text mining network-based approach. We study the narrative structure of the WEB version of 5 books: the Gospel of Matthew, Mark, Luke, John and Acts of the Apostles. The main focus is the protagonists' names interrelationships in an analytical way, namely using various network-based methods and descriptors. This corpus is processed for creating a network: we download the names of people and places from Wikipedia's list of biblical names, then we look for their co-occurrences in each verse and, at the end of this process, we get N co-occurred names. The strength of the link between two names is defined as the sum of the times that these occur together in all the verses, in this way we obtain 5 adjacency matrices (one per book) of N by N couples of names. After this pre-processing phase, for each of the 5 analysed books we calculate the main network centrality measures (classical degree, weighted degree, betweenness and closeness), the network vulnerability and we run the Community Detection algorithm to highlight the role of Messiah inside the overall networks and his groups (communities). We have found that the proposed approach is suitable for highlighting the structures of the names co-occurrences. The found frameworks' structures are useful for interpreting the characters' plots under a structural point of view.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
