Research Output
1 - 10 of 16
Publication
Long Day's Journey into Night: Modernism, Post-Modernism and Maternal Loss
2009, Meaney, Gerardine
Long Day's journey into Night may seem a strange starting place for a feminist analysis of modernism and post-modernism. Yet even the most conservative criticism reads this play as an enactment and embodiment of loss, specifically loss of the mother. That loss is rarely seen in the context of a more general "loss", a cultural loss of legitimacy and authenticity, endemic in and enabling modernism, articulated as "disinheritance" by an Other "coded as feminine."
Publication
Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts
2019-12-04, Leavy, Susan, Meaney, Gerardine, Wade, Karen, Greene, Derek
Publication
Curatr: A Platform for Exploring and Curating Historical Text Corpora
2020-10-23, Greene, Derek, Wade, Karen, Leavy, Susan, Meaney, Gerardine
The increasing availability of digital collections of historical texts presents a wealth of possibilities for new research in the humanities. However, the scale and heterogeneity of such collections raises significant challenges when researchers attempt to find and extract relevant content. This work describes Curatr, an online platform that incorporates domain expertise and methods from machine learning to support the exploration and curation of large historical corpora. We discuss the use of this platform in making the British Library Digital Corpus of 18th and 19th century books more accessible to humanities researchers.
Publication
Dead, White, Male: Irishness in Buffy the Vampire Slayer and Angel
2006, Meaney, Gerardine
Publication
Exploring the Role of Gender in 19th Century Fiction Through the Lens of Word Embeddings
2017-06-20, Grayson, Siobhán, Mulvany, Maria, Wade, Karen, Meaney, Gerardine, Greene, Derek
Within the last decade, substantial advances have been made in the field of computational linguistics, due in part to the evolution of word embedding algorithms inspired by neural network models. These algorithms attempt to derive a set of vectors which represent the vocabulary of a textual corpus in a new embedded space. This new representation can then be used to measure the underlying similarity between words. In this paper, we explore the role an author's gender may play in the selection of words that they choose to construct their narratives. Using a curated corpus of forty-eight 19th century novels, we generate, visualise, and investigate word embedding representations using a list of gender-encoded words. This allows us to explore the different ways in which male and female authors of this corpus use terms relating to contemporary understandings of gender and gender roles.
Publication
History Gasps: Myth in Contemporary Irish Women's Poetry
1995-09-27, Meaney, Gerardine
Recent years have seen a very rapid development in women's poetry in Ireland, a development which is part of a much wider one in women's writing and culture. The prevalence of poetry and the relative scarcity of prose in this movement is specific to Ireland and a significant departure from the pattern elsewhere. The strength of the tradition of women's fiction and the fragmentary nature of the tradition in poetry have tended to produce first an increasingly self-conscious feminist fiction, then an upsurge of women's poetry which attempts to re-define the poetic tradition and women's relation to it.
Publication
Mitigating Gender Bias in Machine Learning Data Sets
2020-07-12, Leavy, Susan, Meaney, Gerardine, Wade, Karen, Greene, Derek
Algorithmic bias has the capacity to amplify and perpetuate societal bias, and presents profound ethical implications for society. Gender bias in algorithms has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine learning, and performing more rigorous analysis of training data. Mitigating bias when algorithms are trained on textual data is particularly challenging given the complex way gender ideology is embedded in language. This paper proposes a framework for the identification of gender bias in training data for machine learning. The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact in the context of search and recommender systems.
Publication
Discovering Structure in Social Networks of 19th Century Fiction
2016-05-25, Grayson, Siobhán, Wade, Karen, Meaney, Gerardine, Rothwell, Jennie, Mulvany, Maria, Greene, Derek
Inspired by the increasing availability of large text corpora online, digital humanities scholars are adopting computational approaches to explore questions in the field of literature from new perspectives. In this paper, we examine detailed social networks of characters, extracted from several works of 19th century fiction by Jane Austen and Charles Dickens. This allows us to apply methodologies from social network analysis, such as community detection, to explore the structure of these networks. By evaluating the results in collaboration with literary scholars, we find that the structure of the character networks can reveal underlying structural aspects within a novel, particularly in relation to plot and characterisation.