William E Underwood

Bio: William E Underwood is an academic researcher. The author has contributed to research in topics: Diction. The author has an hindex of 1, co-authored 1 publications receiving 24 citations.

The Meaning of the Digital Humanities

Abstract: This question of disciplinary meaning—which I ask from the viewpoint of the humanities generally—is larger than the question of disciplinary identity now preoccupying “DH” itself, as insiders call it. Having reached a critical mass of participants, publications, conferences, grant competitions, institutionalization (centers, programs, and advertised jobs), and general visibility, the field is vigorously forming an identity. Recent debates about whether the digital humanities are a “big tent” (Jockers and Worthey), “who's in and who's out?” (Ramsay), whether “you have to know how to code [or be a builder]” (Ramsay, “On Building”), the need for “more hack, less yack” (Cecire, “When Digital Humanities”; Koh), and “who you calling untheoretical?” (Bauer) witness a dialectics of inclusion and exclusion not unlike that of past emergent fields. An ethnographer of the field, indeed, might take a page from Claude Levi-Strauss and chart the current digital humanities as something like a grid of affiliations and differences between neighboring tribes. Exaggerating the differences somewhat, as when a tribe boasts its uniqueness, we can thus say that the digital humanities—much of which affiliates with older humanities disciplines such as literature, history, classics, and the languages; with the remediation of older media such as books and libraries; and ultimately with the value of the old itself (history, archives, the curatorial mission)—are not the tribe of “new media studies,” under the sway of the design, visual, and media arts; Continental theory; cultural criticism; and the avant-garde new. Similarly, despite significant trends toward networked and multimodal work spanning social, visual, aural, and haptic media, much of the digital humanities focuses on documents and texts in a way that distinguishes the field's work from digital research in media studies, communication studies, information studies, and sociology. And the digital humanities are exploring new repertoires of interpretive or expressive “algorithmic criticism” (the “second wave” of the digital humanities proclaimed in “The Digital Humanities Manifesto 2.0” [3]) in a way that makes the field not even its earlier self, “humanities computing,” alleged to have had narrower technical and service-oriented aims. Recently, the digital humanities' limited engagement with identity and social-justice issues has also been seen to be a differentiating trait—for example, by the vibrant #transformDH collective, which worries that the digital humanities (unlike some areas of new media studies) are dominantly not concerned with race, gender, alternative sexualities, or disability.

Quantitative Ansätze in den Literatur- und Geisteswissenschaften : Systematische und historische Perspektiven

Abstract: In its first part, this article gives some illustrative insights into the spectrum of methods and model types from Computational Linguistics that one could in principle apply in the analysis of literary texts. The idea is to indicate the considerable potential that lies in a targeted refinement and extension of the analysis procedures, as they have been typically developed for newspaper texts and other everyday texts. The second part is a personal assessment of some key challenges for the integration of working practices from Computational Linguistics and Literary Studies, which ultimately leads to a plea for an approach that derives the validity of model-based empirical text analysis from the annotation of reference corpus data. This approach should make it possible, in perspective, to refine modeling techniques from Computational Linguistics in such a way that even complex hypotheses from Literary Theory can be addressed with differential, data-based experiments, which one should ideally be able to integrate into a hermeneutic argumentation.

The Longue Durée of Literary Prestige

Abstract: A history of literary prestige needs to study both works that achieved distinction and the mass of volumes from which they were distinguished. To understand how those patterns of preference changed across a century, we gathered two samples of English-language poetry from the period 1820–1919: one drawn from volumes reviewed in prominent periodicals and one selected at random from a large digital library (in which the majority of authors are relatively obscure). The stylistic differences associated with literary prominence turn out to be quite stable: a statistical model trained to distinguish reviewed from random volumes in any quarter of this century can make predictions almost as accurate about the rest of the period. The “poetic revolutions” described by many histories are not visible in this model; instead, there is a steady tendency for new volumes of poetry to change by slightly exaggerating certain features that defined prestige in the recent past.

PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry

Abstract: Most approaches to emotion analysis of social media, literature, news, and other domains focus exclusively on basic emotion categories as defined by Ekman or Plutchik. However, art (such as literature) enables engagement in a broader range of more complex and subtle emotions. These have been shown to also include mixed emotional responses. We consider emotions in poetry as they are elicited in the reader, rather than what is expressed in the text or intended by the author. Thus, we conceptualize a set of aesthetic emotions that are predictive of aesthetic appreciation in the reader, and allow the annotation of multiple labels per line to capture mixed emotions within their context. We evaluate this novel setting in an annotation experiment both with carefully trained experts and via crowdsourcing. Our annotation with experts leads to an acceptable agreement of k = .70, resulting in a consistent dataset for future large scale analysis. Finally, we conduct first emotion classification experiments based on BERT, showing that identifying aesthetic emotions is challenging in our data, with up to .52 F1-micro on the German subset. Data and resources are available at https://github.com/tnhaider/poetry-emotion.