Conflict and revolt in the name of unity: Florentine factions in the Consulte e Pratiche on the cusp of the Ciompi Revolt

Schoots, Jonathan; Rohr, Benjamin; Prajda, Katalin; Padgett, John F.

doi:10.1016/j.poetic.2019.101386

Cited by 4 publications

(1 citation statement)

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“…The meaning of a word is a fundamental object of study in computational linguistics, where researchers have in particular focused on the disambiguation and identification of lexical semantic change (e.g., Schlechtweg, Hätty, et al 2019;Tahmasebi, Borin, and Jatowt 2019) and word senses (e.g., Amrami and Goldberg 2019;Başkaya et al 2013;Daille et al 2016). Similarly, changes in meaning structures have been examined in the field of computational sociology (e.g., Kozlowski, Taddy, and Evans 2020;Linzhuo, Lingfei, and James 2020;Padgett et al 2020;Rule, Cointet, and Bearman 2015;Schoots et al 2020), although these latter studies are not based on deep language models and therefore do not take context or polysemy into account.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Text analysis and deep learning: A network approach

Marquart¹

2021

Preprint

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Much information available to applied researchers is contained within written language or spoken text. Deep language models such as BERT have achieved unprecedented success in many applications of computational linguistics. However, much less is known about how these models can be used to analyze existing text. We propose a novel method that combines transformer models with network analysis to form a self-referential representation of language use within a corpus of interest. Our approach produces linguistic relations strongly consistent with the underlying model as well as mathematically well-defined operations on them, while reducing the amount of discretionary choices of representation and distance measures. It represents, to the best of our knowledge, the first unsupervised method to extract semantic networks directly from deep language models. We illustrate our approach in a semantic analysis of the term "founder". Using the entire corpus of Harvard Business Review from 1980 to 2020, we find that ties in our network track the semantics of discourse over time, and across contexts, identifying and relating clusters of semantic and syntactic relations. Finally, we discuss how this method can also complement and inform analyses of the behavior of deep learning models.

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