King's College London Digital Humanities

Event organised by the Computational Humanities research group.

To register to the seminar, please fill in this form by 1 December 2024.

10 December 2024 – 1.10pm GMT

Remote – Via Microsoft Teams.

Colin Swaelens (Ghent University), Part-of-Speech Tagging & Lemmatisation in Unedited Greek: Simple Tasks, Complex Challenges? 

Abstract

In today’s landscape of language technology, dominated by large language models, tasks like part-of-speech tagging and lemmatisation receive less attention in current NLP research. However, these tasks still pose significant challenges, especially for under-resourced, morphologically rich languages like Ancient Greek. Our project focuses on the verbatim transcriptions of Byzantine marginal poetry stored in the Database of Byzantine Book Epigrams (DBBE). Due to the highly interconnected nature of the poems, we aim to eventually perform similarity detection across the corpus. As a first step, we sought to annotate the DBBE with part-of-speech tags, morphological analyses, and lemmas. Although research on these tasks dates back to more straightforward rule-based systems from the 1970s, current taggers struggle with these unedited texts. The inconsistent orthography —largely due to itacism— adds to this complexity. To mitigate these issues, we trained a transformer-based language model encompassing classical, medieval, and modern Greek. Our experiments, however, revealed that fine-tuning the model for each annotation task was not always fruitful. There is a growing tendency to address such challenges with a multi-task head, allowing the model to process multiple annotations concurrently, drawing inspiration from cognitive psychology. This raises the question: will this more intricate solution outshine the seemingly more transparent methods of the past?

Bio

Colin Swaelens is a PhD student at the Language & Translation Technology Team (LT3) and the Database of Byzantine Book Epigrams (DBBE) at Ghent University, under supervision of dr. Ilse De Vos (Flanders AI Academy) and prof. Els Lefever (LT3). His PhD project is embedded in the project Interconnected texts: a graph-based computational approach to Byzantine paratexts as nodes between textual transmission and cultural and linguistic developments. Within this project, he is developing an annotation pipeline to provide all texts in DBBE with a part-of-speech tag, morphological analysis and lemma. This linguistic information will, in a next stage, be used within the development of a tool to detect similar verses in this corpus, serving the other subprojects on manuscript culture and formulaicity. 

Event organised by the Computational Humanities research group.

To register to the seminar, please fill in this form by 3 November 2024.

12 November 2024 – 1.10pm GMT

Remote – Via Microsoft Teams.

Miguel Escobar (NUS Singapore), Computational theatre research: leveraging large datasets and AI for the performing arts 

Abstract

Computational methods can better help us understand the history and current landscape of the performing arts. For example, we can use network analysis and simulations to study how collaborations within theatre companies develop over time, and how specific management decisions lead to different collaborative patterns. For this, we can take advantage of the records of theatre productions, which are increasingly available in digitized form. Recent advances in open-source AI models can also be used to extract detailed information from videos and texts. For example, we can fine-tune action segmentation models to identify culturally-specific performing conventions in video recordings of performances, and determine how their usage has changed over time. 

Bio

Miguel Escobar Varela is Associate Professor at the department of English, Linguistics, and Theatre Studies (ELTS) and deputy director of the Centre for Computational Social Science and Humanities (CSSH) at the National University of Singapore. In his research, he uses digital tools to document and study cultural heritage in Southeast Asia. He is the author of Theater as data (University of Michigan Press, 2021) and has written several articles on digital humanities and Indonesian theatre. A full list of his publications and digital projects is available at https://miguelescobar.com. He is also Associate Editor of the newly established journal Computational Humanities Research (Cambridge University Press). 

Event organised by the Computational Humanities research group.

To register to the seminar, please fill in this form by 18 October 2024.

22 October 2024 – 3pm BST

In person – King’s College London, Bush House (SE)1.10 (FOR KCL STAFF AND STUDENTS ONLY)

Remote – Via Microsoft Teams

Luca Marinelli (Queen Mary University of London), Gender-Coded Sound: Analysing the Gendering of Music in Toy Commercials via Multi-Task Learning

Abstract

Music can convey ideological stances, and gender is just one of them. Evidence from musicology and psychology research shows that gender-loaded messages can be reliably encoded and decoded via musical sounds. However, much of this evidence comes from examining music in isolation, while studies of the gendering of music within multimodal communicative events are sparse. In this paper, we outline a method to automatically analyse how music in TV advertising aimed at children may be deliberately used to reinforce traditional gender roles. Our dataset of 606 commercials included music-focused mid-level perceptual features, multimodal aesthetic emotions, and content analytical items. Despite its limited size, and because of the extreme gender polarisation inherent in toy advertisements, we obtained noteworthy results by leveraging multi-task transfer learning on our densely annotated dataset. The models were trained to categorise commercials based on their intended target audience, specifically distinguishing between masculine, feminine, and mixed audiences. Additionally, to provide explainability for the classification in gender targets, the models were jointly trained to perform regressions on emotion ratings across six scales, and on mid-level musical perceptual attributes across twelve scales. Standing in the context of MIR, computational social studies and critical analysis, this study may benefit not only music scholars but also advertisers, policymakers, and broadcasters.

Bio

Luca is a PhD student at the UKRI CDT in Artificial Intelligence and Music at the Centre for Digital Music (C4DM), Queen Mary University of London, under the co-supervision of Dr C. Saitis, G. Fazekas, and Prof. P. Lucht (Center for Interdisciplinary Women’s and Gender Studies, Technical University of Berlin). His PhD project sits at the intersection of music data science, gender and media studies, aiming at implementing machine learning techniques to aid the critical analysis of gendered markers in large corpora of television adverts.