Adam Hammond


2022

pdf bib
The Project Dialogism Novel Corpus: A Dataset for Quotation Attribution in Literary Texts
Krishnapriya Vishnubhotla | Adam Hammond | Graeme Hirst
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We present the Project Dialogism Novel Corpus, or PDNC, an annotated dataset of quotations for English literary texts. PDNC contains annotations for 35,978 quotations across 22 full-length novels, and is by an order of magnitude the largest corpus of its kind. Each quotation is annotated for the speaker, addressees, type of quotation, referring expression, and character mentions within the quotation text. The annotated attributes allow for a comprehensive evaluation of models of quotation attribution and coreference for literary texts.

2019

pdf bib
Are Fictional Voices Distinguishable? Classifying Character Voices in Modern Drama
Krishnapriya Vishnubhotla | Adam Hammond | Graeme Hirst
Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

According to the literary theory of Mikhail Bakhtin, a dialogic novel is one in which characters speak in their own distinct voices, rather than serving as mouthpieces for their authors. We use text classification to determine which authors best achieve dialogism, looking at a corpus of plays from the late nineteenth and early twentieth centuries. We find that the SAGE model of text generation, which highlights deviations from a background lexical distribution, is an effective method of weighting the words of characters’ utterances. Our results show that it is indeed possible to distinguish characters by their speech in the plays of canonical writers such as George Bernard Shaw, whereas characters are clustered more closely in the works of lesser-known playwrights.

2018

pdf bib
Deep-speare: A joint neural model of poetic language, meter and rhyme
Jey Han Lau | Trevor Cohn | Timothy Baldwin | Julian Brooke | Adam Hammond
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we propose a joint architecture that captures language, rhyme and meter for sonnet modelling. We assess the quality of generated poems using crowd and expert judgements. The stress and rhyme models perform very well, as generated poems are largely indistinguishable from human-written poems. Expert evaluation, however, reveals that a vanilla language model captures meter implicitly, and that machine-generated poems still underperform in terms of readability and emotion. Our research shows the importance expert evaluation for poetry generation, and that future research should look beyond rhyme/meter and focus on poetic language.

2016

pdf bib
Bootstrapped Text-level Named Entity Recognition for Literature
Julian Brooke | Adam Hammond | Timothy Baldwin
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2015

pdf bib
GutenTag: an NLP-driven Tool for Digital Humanities Research in the Project Gutenberg Corpus
Julian Brooke | Adam Hammond | Graeme Hirst
Proceedings of the Fourth Workshop on Computational Linguistics for Literature

pdf bib
Building a Lexicon of Formulaic Language for Language Learners
Julian Brooke | Adam Hammond | David Jacob | Vivian Tsang | Graeme Hirst | Fraser Shein
Proceedings of the 11th Workshop on Multiword Expressions

pdf bib
Distinguishing Voices in The Waste Land using Computational Stylistics
Julian Brooke | Adam Hammond | Graeme Hirst
Linguistic Issues in Language Technology, Volume 12, 2015 - Literature Lifts up Computational Linguistics

T. S. Eliot’s poem The Waste Land is a notoriously challenging example of modernist poetry, mixing the independent viewpoints of over ten distinct characters without any clear demarcation of which voice is speaking when. In this work, we apply unsupervised techniques in computational stylistics to distinguish the particular styles of these voices, offering a computer’s perspective on longstanding debates in literary analysis. Our work includes a model for stylistic segmentation that looks for points of maximum stylistic variation, a k-means clustering model for detecting non-contiguous speech from the same voice, and a stylistic profiling approach which makes use of lexical resources built from a much larger collection of literary texts. Evaluating using an expert interpretation, we show clear progress in distinguishing the voices of The Waste Land as compared to appropriate baselines, and we also offer quantitative evidence both for and against that particular interpretation.

2013

pdf bib
A Tale of Two Cultures: Bringing Literary Analysis and Computational Linguistics Together
Adam Hammond | Julian Brooke | Graeme Hirst
Proceedings of the Workshop on Computational Linguistics for Literature

pdf bib
Clustering Voices in The Waste Land
Julian Brooke | Graeme Hirst | Adam Hammond
Proceedings of the Workshop on Computational Linguistics for Literature

2012

pdf bib
Unsupervised Stylistic Segmentation of Poetry with Change Curves and Extrinsic Features
Julian Brooke | Adam Hammond | Graeme Hirst
Proceedings of the NAACL-HLT 2012 Workshop on Computational Linguistics for Literature