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The fictionality of topic modeling: Machine reading Anthony Trollope's Barsetshire series

机译:主题建模的虚构性:机器阅读Anthony Trollope的Barsetshire系列

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This essay describes how using unsupervised topic modeling (specifically the latent Dirichlet allocation topic modeling algorithm in MALLET) on relatively small corpuses can help scholars of literature circumvent the limitations of some existing theories of the novel. Using an example drawn from work on Victorian novelist Anthony Trollope's Barsetshire series, it argues that unsupervised topic modeling's counter-factual and retrospective reconstruction of the topics out of which a given set of novels have been created allows for a denaturalizing and unfamiliar (though crucially not ?¢????objective?¢???? or ?¢????unbiased?¢????) view. In other words, topic models are fictions, and scholars of literature should consider reading them as such. Drawing on one aspect of Stephen Ramsay's idea of algorithmic criticism, the essay emphasizes the continuities between ?¢????big data?¢???? methods and techniques and longer-standing methods of literary study.
机译:本文介绍了如何在相对较小的语料库上使用无监督主题建模(特别是MALLET中的潜在Dirichlet分配主题建模算法)可以帮助文学学者绕开该小说一些现有理论的局限性。以维多利亚时代小说家安东尼·特罗洛普(Anthony Trollope)的巴塞特郡(Barsetshire)系列作品为例,该论文认为,无监督主题建模对主题的反事实和回顾性重构(从中得出了一组给定的小说)允许不自然和陌生(尽管关键是???客观?????????????换句话说,主题模型是虚构的,文学学者应考虑按原样阅读它们。本文借鉴了斯蒂芬·拉姆齐(Stephen Ramsay)算法批评的思想,强调了“大数据”与“大数据”之间的连续性。方法和技巧以及文学研究的长期方法。

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