Book of Abstracts - phase 14 - elektroninen.indd - Oulu

Digital Humanities 2008
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2,000 sentences), and thus to develop a model that can classify
narration and description in “the wild”—a corpus of over 800
American novels from 1789 to 1875. This paper will discuss
how we have refi ned our research problem and developed
our classifying model—trial-and-error processes both; our
initial results in “the wild”; and fi nally how macro-analysis of
this kind leads to new problems for literary history.
Existing scholarship suggests that “realist” description enters
the American novel with the historical novel; thus our initial
training set of samples was taken from 10 American historical
novels from the 1820s, 1830s, and 1840s (by J.F. Cooper and
his rivals). Participants in the Beyond Search workshop have
tagged random selections from these 10 novels. The unit of
selection is, for convenience, the chapter. Selected chapters
are broken (tokenized) into individual sentences and humantagged
using a custom XML schema that allows for a “type”
attribute for each sentence element. Possible values for the
type attribute include “Description,” “Narration,” “Both,”
“Speech,” and “Other.” Any disagreement about tagging the
training set has been resolved via consensus. (Since the signals
for description may change over time—indeed, no small
problem for this study—we plan to add an additional training
sample from later in the corpus.)
Using a maximum-entropy classifi er we have begun to
investigate the qualities of the evolving training set and to
identify the stylistic “signals” that are unique to, or most
prevalent in, narrative and descriptive sentences. In the case
of description, for example, we fi nd a marked presence of
spatial prepositions, an above average percentage of nouns
and adjectives, a relatively low percentage of fi rst and second
person pronouns, above average sentence lengths, and a high
percentage of diverse words (greater lexical richness). From
this initial work it has become clear, however, that our fi nal
model will need to include not simply word usage data, but also
grammatical and lexical information, as well as contextualizing
information (i.e., the kinds of sentence that precede and follow
a given sentence, the sentence’s location in a paragraph). We
are in the process of developing a model that makes use of
part of speech sequences and syntactic tree structures, as well
as contextualizing information.
After a suitable training set has been completed and an
accurate classifying model has been constructed, our intention
is to “auto-tag” the entire corpus at the level of sentence.
Once the entire corpus has been tagged, a straightforward
quantitative analysis of the relative frequency of sentence
types within the corpus will follow. Here, the emphasis will be
placed on a time-based evaluation of description as a feature of
19th-century American fi ction. But then, if a pattern emerges,
we will have to explain it—and strictly quantitative analysis
will need to be supplemented by qualitative analysis, as we
interrogate not just what mode is prevalent when, but what
the modes might mean at any given time and how the modes
themselves undergo mutation.
Abstract 3: “Tracking the ‘Voice of
Doxa’ in the Victorian Novel.”
Sarah Allison
The nineteenth-century British novel is known for its
moralizing: is it possible to defi ne this “voice of doxa,” or
conventional wisdom, in terms of computable, sentence-level
stylistic features? A familiar version of the voice of doxa is
the “interrupting narrator,” who addresses the reader in the
second person in order to clarify the meaning of the story.
This project seeks to go beyond simple narrative interruption
to the explication of ethical signals emitted in the process of
characterization (how the portrayal of a character unfolds
over the course of a novel). It also takes a more precise
look at the shift in tense noted by narratologists from the
past tense of the story to the present-tense of the discourse,
in which meaning can be elaborated in terms of proverbs or
truisms. (An example from Middlemarch: “[Fred] had gone
to his father and told him one vexatious affair, and he had left
another untold: in such cases the complete revelation always
produces the impression of a previous duplicity” 23). This
project is the fi rst attempt to generate a set of micro- stylistic
features that indicate the presence of ethical judgment.
Through an analysis of data derived through ad hoc harvesting
of frequently occurring lexical and syntactic patterns (e.g. word
frequencies, part of speech saturation, frequent grammatical
patterns, etc) and data derived through the application of
supervised classifi cation algorithms this research attempts to
determine a set of grammatical features that tend to cluster
around these moments of direct narrative discourse.
This research is an alternative application of the method
developed in Joe Shapiro’s Beyond Search workshop project
(see abstract above), which seeks to identify computable
stylistic differences between narrative and descriptive prose in
19th century American fi ction. In this work we seek to create
another category within the “descriptive,” a subcategory that
captures moments of explicitly moralized description: the
Voice of Doxa. We identify the formal aspects of this authorial
“voice” in order to “hunt” for similar moments, or occurrences,
in a series of novels. The work begins with a limited search
for patterns of characterization evident among characters in a
single George Eliot novel; from this we develop a model, which
we will then apply to the entire Eliot corpus. In the end, the
target corpus is extended to include 250 19th century British
novels wherein we roughly chart the evolutionary course of
the “ethical signal.”
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