通过优惠券收集类型索引的作者归属

IF 0.7 2区 文学 0 LANGUAGE & LINGUISTICS Journal of Quantitative Linguistics Pub Date : 2020-10-01 DOI:10.1080/09296174.2019.1577939
Lukun Zheng, Huiqiang Zheng
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引用次数: 8

摘要

作者归属是指根据选定的文体特征,通过捕捉作者的写作风格来确定相关文本的作者的过程。在本文中,我们提出了一种新的作者归因方法,该方法基于与广义优惠券收集器问题相关的索引简档,称为优惠券收集器类型索引。优惠券收集者问题及其推广具有传统意义和重复性。优惠券是从包含n种不同类型优惠券的人群中一次抽取一张。该过程继续进行,直到获得一整套n个不同的优惠券,并记录抽奖总数。我们的方法论建立在虚词的基础上。我们通过使用经验自举技术构建优惠券收集器类型指数的置信区间来建立测试程序。我们使用几个作者已知的写作样本来验证我们提出的方法。然后,我们应用这种方法来探讨谁写了《奥兹国》系列的第十五本书的问题,莱曼·弗兰克·鲍姆(1856-1919)和他的继任者鲁思·普卢姆利·汤普森(1891-1976)对其作者身份存在争议。
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Authorship Attribution via Coupon-Collector-Type Indices
ABSTRACT Authorship attribution is the process of determining the author of a text in question by capturing an author’s writing style based on selected stylistic features. In this paper, we propose a new methodology for authorship attribution based on a profile of indices related to the generalized coupon collector problem, called coupon-collector-type indices. The coupon collector problem and its generalizations are of traditional and recurrent interests. Coupons are drawn one at a time from a population containing n distinct type of coupons. The process continues until a complete set of n distinct coupons is obtained and the total number of draws, , is recorded. We base our methodology on function words. We establish a testing procedure by constructing a confidence band of the coupon-collector-type indices using an empirical bootstrap technique. We validate our proposed methodology using several writing samples whose authorship is known. We then apply this methodology to explore the question of who wrote the fifteenth Oz book, whose authorship is disputed between Lyman Frank Baum (1856–1919) and his successor) on the Oz series, Ruth Plumly Thompson (1891–1976).
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来源期刊
CiteScore
2.90
自引率
7.10%
发文量
7
期刊介绍: The Journal of Quantitative Linguistics is an international forum for the publication and discussion of research on the quantitative characteristics of language and text in an exact mathematical form. This approach, which is of growing interest, opens up important and exciting theoretical perspectives, as well as solutions for a wide range of practical problems such as machine learning or statistical parsing, by introducing into linguistics the methods and models of advanced scientific disciplines such as the natural sciences, economics, and psychology.
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