Adam Hammond

GutenTag: an NLP-driven Tool for Digital Humanities Research in the Project Gutenberg Corpus

This paper introduces a software tool, GutenTag, which is aimed at giving literary researchers direct access to NLP techniques for the analysis of texts in the Project Gutenberg corpus. We discuss several facets of the tool, including the handling of formatting and structure, the use and expansion of metadata which is used to identify relevant subcorpora of interest, and a general tagging framework which is intended to cover a wide variety of future NLP modules. Our hope that the shared ground created by this tool will help create new kinds of interaction between the computational linguistics and digital humanities communities, to the benefit of both.

Bootstrapped Text-level Named Entity Recognition for Literature

We present a named entity recognition (NER) system for tagging fiction: LitNER. Relative to more traditional approaches, LitNER has two important properties: (1) it makes no use of handtagged data or gazetteers, instead it bootstraps a model from term clusters; and (2) it leverages multiple instances of the same name in a text. Our experiments show it to substantially outperform off-the-shelf supervised NER systems.

Building a Lexicon of Formulaic Language for Language Learners

Though the multiword lexicon has long been of interest in computational linguistics, most relevant work is targeted at only a small portion of it. Our work is motivated by the needs of learners for more comprehensive resources reflecting formulaic language that goes beyond what is likely to be codified in a dictionary. Working from an initial sequential segmentation approach, we present two enhancements: the use of a new measure to promote the identification of lexicalized sequences, and an expansion to include sequences with gaps. We evaluate using a novel method that allows us to calculate an estimate of recall without a reference lexicon, showing that good performance in the second enhancement depends crucially on the first, and that our lexicon conforms much more with human judgment of formulaic language than alternatives.

Using models of lexical style to quantify free indirect discourse in modernist fiction

Modernist authors such as Virginia Woolf and James Joyce greatly expanded the use of ‘free indirect discourse’, a form of third-person narration that is strongly influenced by the language of a viewpoint character. Unlike traditional approaches to analyzing characterization using common words, such as those based on Burrows (1987), the nature of free indirect discourse and the sparseness of our data require that we understand the stylistic connotations of rarer words and expressions which cannot be gleaned directly from our target texts. To this end, we apply methods introduced in our recent work to derive information with regards to six stylistic aspects from a large corpus of texts from Project Gutenberg. We thus build high-coverage, finely grained lexicons that include common multiword collocations. Using this information along with student annotations of two modernist texts, Woolf’s To The Lighthouse and Joyce’s The Dead , we confirm that free indirect discourse does, at a stylistic level, reflect a mixture of narration and direct speech, and we investigate the extent to which social attributes of the various characters (in particular age, class, and gender) are reflected in their lexical stylistic profile.

Modeling Modernist Dialogism: Close Reading with Big Data

In Macroanalysis (2013), Matthew Jockers provocatively declares that large digitized collections of literary texts have rendered close reading “totally inappropriate as a method of studying literary history.” Hammond, Brooke and Hirst respond by demonstrating the productive interpretive interplay that results when close reading is placed in a “feedback loop” with the insights available at the scale of big data. Using cutting-edge techniques in computational stylistics, including their own six-dimensional approach to quantifying literary style, Hammond, Brooke and Hirst argue that analytic techniques trained on large datasets can prompt new close readings and, in particular, provide new insight into the dialogism or multi-voicedness of three important modernist texts: T. S. Eliot’s The Waste Land, Virginia Woolf’s To the Lighthouse and James Joyce’s “The Dead.”