Literary Pattern Recognition: Modernism between Close Reading and Machine Learning

Long, Hoyt; So, Richard Jean

doi:10.1086/684353

Cited by 39 publications

(17 citation statements)

References 12 publications

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“…I myself am interested in using machine learning to explore the authorship of the Plum in the Golden Vase (Vierthaler, 2016b). Richard Jean So and Hoyt Long have been at the forefront of cross‐linguistic/cultural digital humanities and have coauthored multiple important pieces that touch on Japanese, Chinese, and English literature and history (Long & So, 2013, 2016a, 2016b). Long also coauthored a piece with Anatoly Detwyler and Yuancheng Zhu on “Self‐repetition and East Asian Literary Modernity” (Long, Detwyler, & Zhu, 2018).…”

Section: Researchmentioning

confidence: 99%

Digital humanities and East Asian studies in 2020

Vierthaler

2020

History Compass

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Although East Asian studies scholars have a long history of using technology to their advantage, starting in at least the 1970s, East Asian digital humanities lags behind digital humanities in other parts of the world. This gap is rapidly closing, and a variety of new resources, tools, and communities have developed in the last few years. At the same time, this progress has produced a remarkable flowering of research. This article provides a broad overview of some of the more recent and important trends in East Asian digital humanities. I discuss a variety of important datasets that include text corpora, geographic information, biographic data, images, and more. I also introduce a number of analytical tools, discuss community building efforts, and discuss some of the research that has emerged from this. Finally, I address efforts in digital humanities education, because although East Asian scholars are rapidly adopting digital tools, the field still faces a gap in knowledge driven by a lack of easily accessible training opportunities. 1 | INTRODUCTION There have been significant developments in the application of the digital humanities (DH) to East Asian studies in recent years. The field is rapidly growing, attracting interest from funding bodies, cultural heritage institutions, and scholars from undergraduate students to full professors. Digital East Asian studies has gone through a few distinct phases, marked by ever expanding areas of inquiry and increased technical sophistication. The East Asian studies community has been developing digital infrastructure, datasets, and tools since long before the term "digital humanities" was coined. As computers have become more widespread, new resources and platforms are making information An older version of this article focused on North American and European Chinese digital humanities. This was translated by Song Yingchun (宋迎春) and published in Chinese in 2018 in Shandong Social Sciences (Vierthaler, 2018). I have also written on the state of Chinese digital humanities elsewhere (Vierthaler, 2016c).

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Section: Researchmentioning

confidence: 99%

Digital humanities and East Asian studies in 2020

Vierthaler

2020

History Compass

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“…Most previous work within the NLP community applies distant reading (Jockers, 2013) to large collections of books, focusing on modeling different aspects of narratives such as plots and event sequences (Chambers and Jurafsky, 2009;McIntyre and Lapata, 2010;Goyal et al, 2010;Eisenberg and Finlayson, 2017), characters (Bamman et al, 2014;Iyyer et al, 2016;Chaturvedi et al, , 2017, and narrative similarity (Chaturvedi et al, 2018). In the same vein, researchers in computational literary analysis have combined statistical techniques and linguistics theories to perform quantitative analysis on large narrative texts (Michel et al, 2011;Franzosi, 2010;Underwood, 2016;Jockers and Kirilloff, 2016;Long and So, 2016), but these attempts largely rely on techniques such as word counting, topic modeling, and naive Bayes classifiers and are therefore not able to capture the meaning of sentences or paragraphs (Da, 2019). While these works discover general patterns from multiple literary works, we are the first to use cutting-edge NLP techniques to engage with specific literary criticism about a single narrative.…”

Section: Related Workmentioning

confidence: 99%

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Wang

Iyyer

2019

Proceedings of the 2019 Conference of the North

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Literary critics often attempt to uncover meaning in a single work of literature through careful reading and analysis. Applying natural language processing methods to aid in such literary analyses remains a challenge in digital humanities. While most previous work focuses on "distant reading" by algorithmically discovering high-level patterns from large collections of literary works, here we sharpen the focus of our methods to a single literary theory about Italo Calvino's postmodern novel Invisible Cities, which consists of 55 short descriptions of imaginary cities. Calvino has provided a classification of these cities into eleven thematic groups, but literary scholars disagree as to how trustworthy his categorization is. Due to the unique structure of this novel, we can computationally weigh in on this debate: we leverage pretrained contextualized representations to embed each city's description and use unsupervised methods to cluster these embeddings. Additionally, we compare results of our computational approach to similarity judgments generated by human readers. Our work is a first step towards incorporating natural language processing into literary criticism.

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“…For example, in a landmark study, Mosteller and Wallace (1963) analyzed the authorship of The Federalist Papers using a statistical text analysis by focusing on style, based on the distribution of function words, rather than content. As another example, Long and So (2016) studied what defines English haiku and showed how computational analysis and close reading can complement each other. Computational approaches are valuable precisely because they help us identify patterns that would not otherwise be discernible.…”

Section: Introductionmentioning

confidence: 99%

How We Do Things With Words: Analyzing Text as Social and Cultural Data

Nguyen

Liakata

DeDeo

et al. 2020

Front. Artif. Intell.

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In this article we describe our experiences with computational text analysis involving rich social and cultural concepts. We hope to achieve three primary goals. First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods. Second, we hope to provide a set of key questions that can guide work in this area. Our guidance is based on our own experiences and is therefore inherently imperfect. Still, given our diversity of disciplinary backgrounds and research practices, we hope to capture a range of ideas and identify commonalities that resonate for many. This leads to our final goal: to help promote interdisciplinary collaborations. Interdisciplinary insights and partnerships are essential for realizing the full potential of any computational text analysis involving social and cultural concepts, and the more we bridge these divides, the more fruitful we believe our work will be.

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Literary Pattern Recognition: Modernism between Close Reading and Machine Learning

Cited by 39 publications

References 12 publications

Digital humanities and East Asian studies in 2020

Digital humanities and East Asian studies in 2020

Untitled

How We Do Things With Words: Analyzing Text as Social and Cultural Data

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