Introduction The use of textual data in humanities research is significantly
aided by automated techniques such as
key word analysis, collocations and corpus annotation
(e.g. part-of-speech). If a text corpus contains a large
amount of spelling variation, there is a considerable
impact on the accuracy of these automatic techniques.
For example, studies in respect to Early Modern English
(EModE) corpora - the focus of the study detailed in this
paper - have documented the adverse effects of spelling
variation on key word analysis (Baron et al., 2009), partof-
speech tagging (Rayson et al., 2007) and semantic
analysis (Archer et al., 2003).
The problem of spelling variation in corpora needs to
be addressed in order for more accurate and meaningful
results to be achieved in fields where historical source
texts are required. Researchers can side-step the issue
by using modernized versions of corpora, of course, but
these are not always available. Another potential solution
is to manually standardize the spellings; this includes
reading through texts, spotting any non-standard
spellings and deciding upon a modern equivalent, resulting
in the production of a new version of the text with
spelling variants replaced. However, a manual standardizing
approach is likely to be unworkable when working
with some of the larger corpora or online databases that
are now available.
This paper details the current version of the VARiant Detector
(VARD 2) tool, which can be used in various ways
to standardize spelling variation in corpora. In particular,
the tool can be used to (partially) standardize spellings
automatically, with no restriction on the number of
words to be processed. Here, we focus on the ways in
which the tool can be trained from manually standardized
corpora samples, particularly the letter replacement
component of the tool, and evaluate the improvement
that this makes to the performance of VARD 2.
Early Modern English Spelling Variation
The EModE period is of particular interest in historical
text mining studies; book production increased sharply
during the period, largely due to the introduction of the
printing press (1476) and increasing literacy levels (Görlach,
1991: 6). As such, the EModE period is the earliest
period of the English Language from which a reasonably
large corpus can be constructed and studied in detail.
Spelling variation was a major feature of EModE texts,
the extent of which we have recently quantified in Baron
et al. (2009). It is common to find words spelt in a number
of different forms in the same text or even on the
same page. This was not seen as problematic, however,
as there was no notion of the importance for a single
spelling for each word; for example, letters would be
added or removed to ease line justification. Table 1 below
shows some typical spelling variants found in EModE
texts, whilst Vallins and Scragg (1965) and Culpeper
and Archer (forthcoming) describe the spelling variation
trends in more detail. We have shown the effect of this spelling variation on
textual analysis techniques in previous and forthcoming
papers: key word analysis (Baron et al, 2009), part-ofspeech
tagging (Rayson et al., 2007) and semantic tagging
(Archer et al., 2003). All of the studies showed that
spelling variation causes considerable problems to the
accuracy and meaningfulness of results, and that dealing
with spelling variation (even partially) can achieve
substantial improvements in annotation accuracy.1 The
production of standardized or modernized versions of historical corpora therefore allows for more accurate automated text mining techniques to be applied to the
VARD 2 and DICER
Our solution to the spelling variation problem described
in the previous section has been the development of the
VARD 2 tool,3 a piece of software designed to assist researchers
in standardizing historical corpora (specifically
EModE texts) both manually and automatically. VARD
2 uses a manually created list of variant – replacements
mappings as well as employing methods from modern
spell checking software; such as phonetic matching, letter
replacement heuristics and Edit Distance. An earlier
version of the VARD 2 software is described in more
detail and evaluated in Rayson et al (2008). The current
version can cater for user-created letter replacement
rules, which will be used by the tool to find potential
variant replacements. In addition, XML provision has
been improved, processing speed increased, and a new
word reference list4 added. Screenshots of the latest version,
VARD 2.2, are shown in Fig. 1 and Fig. 2.
Fig. 1 Screenshot of VARD 2.2 showing the interactive
mode which allows the user to manually standardize texts
and train the tool on samples of a corpus
One way in which VARD 2 can be used is to automatically
standardize the spelling variation in an entire corpus.
For EModE texts, this can be done immediately, with
no training. However, for better results and to use the
tool with other varieties of English, the user can train the
software on a particular corpus by using the interactive
version to manually process samples from the corpus.
The tool will improve its ability to deal with a corpus
based on decisions made by the user in the interactive
version. It does this by learning which of its methods are
most successful in finding the correct replacement for
variants and adjusting its method weights accordingly
(these are used when ranking potential replacements).
The tool will also edit its dictionary and its list of specific
variant replacements based on changes made by the user.
A new development to allow for further training of
VARD 2 on a corpus is a tool named DICER (Discover
and Investigation of Character Edit Rules). DICER can
search XML output from VARD 2 for variant – replacement
mappings or be provided with a list of such mappings.
Each mapping is analyzed and a set of character
edit rules are produced which can transform the spelling
variant into its modern equivalent. The details of
these character edit rules are then collated into a database,
which can be viewed through a set of web pages.5
The main table produced by the analysis, shown in Fig.
3, displays details of the individual character edit rules
along with various frequencies. By clicking on individual
rules, further information is available such as which
characters typically occur before and after the rule occurs;
this is shown for the rule ‘Delete e’ in Fig. 4. Any
frequency in the tables can be clicked to view a list of occurrences
producing that frequency. The data produced
in the DICER analysis is vast and thus cannot be detailed
in full here. By using DICER to analyze manually standardized samples
of a corpus, a list of common character edit rules can
be viewed. These character edit rules can then be added
to VARD 2 and the tool will be better equipped to make
judgments on the correct replacement for variants found
whilst automatically standardizing the corpus.
In order to test VARD 2 and DICER’s training ability
a 5,000-word sample of Shakespeare’s First Folio6 was
manually standardized in the interactive-mode of VARD
2 as training data, the entire corpus was then automatically
standardized. Using this small amount of training
data (6% of the entire corpus) increased the proportion
of spelling variants replaced7 from 70.33% to 73.75%.
The automatic standardization (after training) resulted
in 10,601 unique variant replacements. 70.35% of these
replacements could be achieved through VARD 2’s original
set of character edit rules alone. DICER analysis
was then produced on the manually standardized Shakespeare
sample; this is shown in Fig. 3. VARD 2’s rule list
was augmented with additional rules from the DICER
analysis: any rule occurring 10 or more times was added,
if not already present. Using this new rule list 77.66% of
the 10,601 unique replacements could now be found, an
increase of 7.31%.
The results are extremely promising, and increasing the
size of the manually standardized sample should improve
these figures even further. DICER can also be used
to provide probabilities dictating how likely a character
edit rule should be applied in a certain position with
specified surrounding characters. Modifying VARD to
use these probabilities could see even greater improvements
Calculating the precision of VARD 2’s methods is difficult
without a manually checked standardized corpus
of decent size. We have recently acquired such a corpus
and present the results of using this corpus to train and
evaluate VARD 2’s methods in Baron and Rayson (forthcoming).
This paper has described the problems that variant
spellings cause for historical text mining, particularly
for automated methods in historical corpus linguistics,
such as part-of-speech tagging and key words analysis.
In previous and forthcoming work, we have quantified
the errors or differences that result from the application
of untrained tools and techniques on historical data that
has not been standardized. Our proposed solution is the
VARD tool, which offers the potential to standardize
spelling in historical texts automatically and with high
accuracy. We have described recent improvements to
VARD 2, such as the inclusion of a much larger modern
dictionary that enables better detection of historical variants
and matching with modern forms.
VARD 2 has been developed to deal with spelling variation
in EModE texts; the tool can be used with its default
settings to achieve partial standardization automatically.
However, with some training, we have shown that
VARD 2’s performance is enhanced. Further training
could allow the tool to be used with other varieties of
non-standard English (e.g. SMS corpora and weblogs).
In the future, we will evaluate the extent to which variation
that can only be detected contextually (e.g. ‘then’
for ‘than’ and ‘bee’ instead of ‘be’) contributes to the
problem. Dealing with this problem requires more advanced
techniques, e.g. POS tagging, to be used in the
1Of course, spelling variants themselves are important
linguistic features and thus worthy of study: as such,
although our focus relates to how we might deal with
spelling variation within historical data as a means of
enabling the (more) effective use of automated analytical
techniques, we advocate that any solution to this problem
should always retain the original spelling. VARD 2
does so using an xml tag to note a replacement with the
original spelling stored as an attribute of the tag.
2It should be noted that the accuracy of annotation is
likely to be affected by additional factors, including
differences in the grammar of the EmodE period when
compared to present-day English (see Kytö and Voutilainen,
1995) and the possibility of a semantic shift in
words from EModE to present-day English (see, for example,
Knapp, 2000). 3The tool is available to download online, with a user
guide also provided. The software is free to use for
academic purposes from http://www.comp.lancs.
4Derived from the Spell Checking Oriented Word List
(SCOWL). See http://wordlist.sourceforge.net/scowlreadme
5Available at http://juilland.comp.lancs.ac.uk/dicer/
6Available from the Oxford Text Archive: http://ota.
7Variants here are words which VARD 2 deems to be
variants, i.e. words which are not in its modern lexicon.
It should be noted that words will be incorrectly marked
as variants (particularly proper names) and some variants
will be incorrectly marked as modern words (particularly
read-word errors, such as ‘bee’ for ‘be’ and ‘doe’
Archer, D., McEnery, T., Rayson, P. and Hardie, A.
(2003). Developing an automated semantic analysis system
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Corpus Linguistics 2003 conference. UCREL technical
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Baron, A. and Rayson, P. (forthcoming). Automatic
standardization of texts with spelling variation, how
much training data do you need? To appear in Corpus
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Vallins, G. H., and Scragg, D. G. (1965). Spelling. André
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