Rational Creatures: Using Vector Space Models to Examine Independence in the Novels of Jane Austen, Maria Edgeworth, and Sydney Owenson (1800–1820)

Recent trends in digital humanities have led to a proliferation of studies that apply ‘distant’ reading to textual data. There is an uneasy relationship between the increased use of computational methods and their application to literary studies. Much of the current literature has focused on the exploration of large corpora. However, the ability to work at this scale is often not within the power (financial or technical) or the interests, of researchers. As these large-scale studies often ignore smaller corpora, few have sought to define a clear theoretical framework within which to study small-scale text collections. In addition, while some research has been carried out on the application of term-document vector space models (topic models and frequency based analysis) to nineteenth century novels, no study exists which applies word-context models (word embeddings and semantic networks) to the novels of Austen, Edgeworth, and Owenson. This study, therefore, seeks to evaluate the use of vector space models when applied to these novels. This research first defines a theoretical framework - enhanced reading - which combines the use of close and distant reading. Using a corpus of twenty-eight nineteenth century novels as its central focus, this study also demonstrates the practical application of this theoretical approach with the additional aim of providing an insight into the authors’ representation of independence at a time of great political and social upheaval in Ireland and the UK. The use of term-document models was found to be, generally, more useful for gaining an overview of the corpora. However, the findings for word-context models reveal their ability to identify specific textual elements, some of which were not readily identified through close reading, and therefore were useful for exploring texts at both corpus and individual text level

Rational Creatures: Using Vector Space Models to Examine Independence in the Novels of Jane Austen, Maria Edgeworth, and Sydney Owenson (1800–1820)

Recent trends in digital humanities have led to a proliferation of studies that apply ‘distant’ reading to textual data. There is an uneasy relationship between the increased use of computational methods and their application to literary studies. Much of the current literature has focused on the exploration of large corpora. However, the ability to work at this scale is often not within the power (financial or technical) or the interests, of researchers. As these large-scale studies often ignore smaller corpora, few have sought to define a clear theoretical framework within which to study small-scale text collections. In addition, while some research has been carried out on the application of term-document vector space models (topic models and frequency based analysis) to nineteenth century novels, no study exists which applies word-context models (word embeddings and semantic networks) to the novels of Austen, Edgeworth, and Owenson. This study, therefore, seeks to evaluate the use of vector space models when applied to these novels. This research first defines a theoretical framework - enhanced reading - which combines the use of close and distant reading. Using a corpus of twenty-eight nineteenth century novels as its central focus, this study also demonstrates the practical application of this theoretical approach with the additional aim of providing an insight into the authors’ representation of independence at a time of great political and social upheaval in Ireland and the UK. The use of term-document models was found to be, generally, more useful for gaining an overview of the corpora. However, the findings for word-context models reveal their ability to identify specific textual elements, some of which were not readily identified through close reading, and therefore were useful for exploring texts at both corpus and individual text level

The Digital Humanities in Ireland

If the digital humanities are to thrive they must be allowed to remain culturally dissonant. The ways in which DH is practiced will differ across national contexts, with each region having peculiarities representative of the culture-specific conditions which shaped the field as it first emerged and later developed. While scholars tend to belong and contribute to international communities of praxis, doing DH in one place might look very different to doing DH somewhere else. Disciplinary cultures are often transnational, but where scholars are trained and where they work will usually impact upon their own, individualised perspective of that discipline. This paper traces the history of the digital humanities in Ireland, providing on account of DH as it exists in a specifically Irish context. It mimics the Busa narrative, uncovering equivalent figures from Irish DH’s origin story, while detailing some of the key initiatives and institutions to have contributed to the national development of the discipline. As a small island with a close-knit academic community, culturally torn between US, British and European influences, Ireland represents an opportunity to examine DH as a national project, and how such a project might be contrasted with international norms, what it achieved, and where it has failed

Other Americans: the racialized and anachronized Appalachian mountaineer at the turn of the twentieth century

The central claim of this project is that literary and historical texts from the turn of the last century rhetorically contained the Southern Appalachian mountaineer through racializing that figure into less-than-normative whiteness and anachronizing that figure into incompatibility with the modern era. Other scholars have traced the origins of Appalachian stereotypes to this foundational period, and some have also pointed to the capitalistic utility of Appalachian stereotypes given the contemporaneous and rather sudden profitability of Appalachian land and labor via the coal and timber industries. I expand upon previous scholarship to examine this phenomenon in terms of exploitative trends in American history and literature. In particular, I draw a parallel between the rhetoric surrounding the supposedly “Vanishing Indian” in the mid-nineteenth century and that of the supposedly doomed mountaineer, hopelessly backward and incapable of modernizing, in the late-nineteenth and early-twentieth centuries. The literary texts that established the hillbilly stereotype—one that has far surpassed the texts themselves in ubiquity—as well as that stereotype’s wide acceptance in historical paratexts of the period demonstrate that mountaineers’ rhetorical exploitation had more than a casual relationship with their material exploitation. In this vein, chapters one through three consider Mary N. Murfree’s In the Tennessee Mountains (1884), Emma Bell Miles’s The Spirit of the Mountains (1905), and John Fox, Jr’s The Trail of the Lonesome Pine (1908), in the context of an emerging racial hierarchy of the late-nineteenth and early-twentieth centuries, which along with denigrating those deemed non-white also privileged and disenfranchised particular kinds of whiteness. Chapter four examines more recent Appalachian literature by Lee Smith and Silas House. Though their novels under consideration here, Fair and Tender Ladies (1988) and A Parchment of Leaves (2001), respectively, were published in the late-twentieth and early-twenty-first centuries, they are set at the turn of the twentieth century, the same period as the earlier literary texts examined in this project. Having at their disposal the effects of land usurpation, Smith and House are able to view the figure of the Southern Appalachian mountaineer over the longue durée, complicating and amending that figure’s earlier characterization. Thus, these authors’ portrayals have something to tell us about the enduring marginalization of the mountaineer and the persistence of historical disenfranchisement. Moreover, while the literature of the late-nineteenth and early-twentieth centuries, I argue, was complicit with the ruinous re-appropriation of mountain lands by greedy industrial interests, the literature of the late-twentieth and early-twenty-first centuries may serve as a tool in rehabilitating the image of the Appalachian mountaineer. Finally, in chapter five, the conclusion, I consider modern popular conceptions of Appalachian people, some of which demonstrate that the hillbilly stereotype and its relationship to economic disenfranchisement persist to this day

Sociolinguistically Driven Approaches for Just Natural Language Processing

Natural language processing (NLP) systems are now ubiquitous. Yet the benefits of these language technologies do not accrue evenly to all users, and indeed they can be harmful; NLP systems reproduce stereotypes, prevent speakers of non-standard language varieties from participating fully in public discourse, and re-inscribe historical patterns of linguistic stigmatization and discrimination. How harms arise in NLP systems, and who is harmed by them, can only be understood at the intersection of work on NLP, fairness and justice in machine learning, and the relationships between language and social justice. In this thesis, we propose to address two questions at this intersection: i) How can we conceptualize harms arising from NLP systems?, and ii) How can we quantify such harms? We propose the following contributions. First, we contribute a model in order to collect the first large dataset of African American Language (AAL)-like social media text. We use the dataset to quantify the performance of two types of NLP systems, identifying disparities in model performance between Mainstream U.S. English (MUSE)- and AAL-like text. Turning to the landscape of bias in NLP more broadly, we then provide a critical survey of the emerging literature on bias in NLP and identify its limitations. Drawing on work across sociology, sociolinguistics, linguistic anthropology, social psychology, and education, we provide an account of the relationships between language and injustice, propose a taxonomy of harms arising from NLP systems grounded in those relationships, and propose a set of guiding research questions for work on bias in NLP. Finally, we adapt the measurement modeling framework from the quantitative social sciences to effectively evaluate approaches for quantifying bias in NLP systems. We conclude with a discussion of recent work on bias through the lens of style in NLP, raising a set of normative questions for future work