Birnbaum, David J., and Elise Thorsen. “Markup and meter: Using XML tools to teach a computer to think about versification.” Presented at Balisage: The Markup Conference 2015, Washington, DC, August 11 - 14, 2015. In Proceedings of Balisage: The Markup Conference 2015. Balisage Series on Markup Technologies, vol. 15 (2015). https://doi.org/10.4242/BalisageVol15.Birnbaum01.
Balisage: The Markup Conference 2015 August 11 - 14, 2015
Balisage Paper: Markup and meter: Using XML tools to teach a computer to think about versification
David J. Birnbaum
Professor and Chair
Department of Slavic Languages and Literatures, University of
Pittsburgh
David J. Birnbaum is Professor and Chair of the Department of Slavic Languages
and Literatures at the University of Pittsburgh. He has been involved in the
study of electronic text technology since the mid-1980s, has delivered
presentations at a variety of electronic text technology conferences, and has
served on the board of the Association for Computers and the Humanities, the
editorial board of Markup languages: Theory and
practice, and the Text Encoding Initiative Council. Much of his
electronic text work intersects with his research in medieval Slavic manuscript
studies, but he also often writes about issues in the philosophy of
markup.
Elise Thorsen
PhD Candidate
Department of Slavic Languages and Literatures, University of
Pittsburgh
Elise Thorsen is a PhD Candidate in the Department of Slavic Languages and
Literatures at the University of Pittsburgh. She has been developing XML-based
tools for the computationally assisted analysis of Russian-language verse, which
she has presented at a number of conference panels dedicated to developing
digital humanities research agenda in Slavic Languages and Literatures.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike
4.0 International License.
Abstract
This paper describes an XML-based system to identify and visualize some of the
structural features of natural-language poetry. Poetic texts are poster children for
overlapping hierarchies, since the organization of poems into cantos, stanzas,
lines, and feet is largely independent of the sentences and words of the text. Foot
boundaries and word boundaries are mutually independent, yet the implementation of
caesura depends on their synchronization. Furthermore, the formal organization of
poetry is not only overlapping, but also massively discontinuous in terms of how
underlying formal structures like meter or rhyme are realized in natural
orthography. In many poetic traditions, stress and pronunciation are only implicit
in written texts, and our first challenge is to identify those structures
automatically and add markup to make them explicit, so that we can then use them to
identify such properties as meter and rhyme. When stress and pronunciation are made
explicit within an XML model, the most natural representations will often involve
mixed content, which poses special challenges for subsequent XML processing.
Quantitative approaches to the study of Russian verse: The research context
Quantitative metrics, and particularly the statistical study of meter and rhyme, has
been a core research methodology in Russian verse theory and scholarship at least
since
the early twentieth century both among Russian scholars (e.g., Belyj 1910, Taranovski 1953, Gasparov 1984) and abroad (e.g.,
Scherr 1986, Shaw 1993, Friedberg 2009, Friedberg 2011).[1] Until recently, the methods used in this research have had to rely largely
on the laborious human identification and tagging or recording within a corpus of
all
individual stress and rhyme phenomena, which have then served as input into the (often
computer-assisted) statistical analysis of synchronic patterns and diachronic trends
in
meter and rhyme. Not only is this sort of manual effort at corpus preparation not
scalable, and therefore also effectively not reproducible, but more often than not
the
raw data underlying published scholarship have not been shared, which means that the
results cannot be replicated and the conclusions cannot be verified for reasons that
transcend labor and scalability. Almost the entire corpus of Russian classical verse
is
now freely accessible on the Internet in authoritative scholarly digital editions
(e.g.,
at FEB and others), and computational tools could therefore be used to
relieve scholars of the human labor previously needed in data collection, preparation,
and analysis for studies in quantitative versification. To the extent that the data
preparation and analysis proceeds algorithmically, intermediate results can be saved
and
examined and the entire process can be replicated and verified.
The principal limitation to using available poetic texts for this purpose has been
that the place of stress in words, which in Russian is not part of standard orthographic
practice and is not predictable without linguistic knowledge, must be determined before
an orthographic representation can be converted algorithmically to a useful phonetic
representation, which is, in turn, a prerequisite for identifying meter and rhyme
automatically. To address this need, the authors have undertaken the development of
a
network of tools, all freely available, that automate as much as possible this process.
The system begins with a full-text dictionary of Russian, consisting of approximately
100,000 headwords with all inflected forms, including information about the place
of
stress, which can be accessed as a web service to add stress information to
Russian-language texts in natural, native orthography. The stressed texts can be used
for other purposes (such as in readers for language learners), but our principal goal
in
developing this module was that they could then serve as input into processes that
are
capable of producing descriptive statistics and visualizations of metrical and rhyme
patterns in individual poetic texts and in corpora. These integrated resources thus
serve as a workstation for the formal and quantitative exploration of Russian
versification in a way that is consistent with current best practice for research
data
management. All data and other materials and resources are available under a Creative
Commons license.
Assumptions and constraints
We are aided in our research by a combination of fortunate accidental properties of
Russian orthography, language, and verse practice, on the one hand, and our application
of simplifying assumptions and constraints, on the other. These include the
following:
There is a one-to-one relationship between vowel letters in Russian
orthography and syllables in Russian pronunciation. Unlike in English, for
example, there are no silent vowels letters and there is no use of sequences of
vowel lettters to represent a single vowel sound. This makes it possible to
identify and count vowel letters, an easily identified formal component of
written Russian verse, as a proxy for
syllables, a phonetic property of spoken
Russian verse that is less directly accessible on the basis of written input.
This approach would not be practical in English, where not all vowel letters
correspond to syllabic nuclei.[2]
With the exception of compound words, Russian pronunciation does not have
secondary stress. Unlike the situation with English, for example, no matter how
long the word form, it cannot have more than one stressed syllable.
Russian spelling is closer to Russian pronunciation than is the case with
English. Normal Russian writing does not mark the stressed vowel
orthographically, but if the place of stress is known (for example, by
retrieving it from the full-text lookup dictionary mentioned above), the
relationship between orthography and pronunciation is simple enough to be
described algorithmically with sufficient precision to identify rhyme and other
sound patterning.
The predominant form of Russian metrical practice since the Golden Age of
Russian poetry (first third of the nineteenth century) and continuing into the
present is formally strict and generally regular syllabotonic verse. In addition
to syllabotonic verse, Russian does have significant syllabic and tonic
traditions, which our system is not designed to process. Instead, we target
fairly regular syllabotonic verse for analysis, and use the regularity of the
ambient meter and rhyme as a heuristic for
making predictions about ambiguities and lacunae that cannot be resolved purely
from information available in our full-text lookup dictionary.
Our system is not designed, then, to analyze poetry that is not syllabotonic
in organization (e.g., free verse, purely syllabic verse, purely tonic verse).
It is also not designed to analyze poetry that has a heterometrical structure,
that is, syllabotonic poems that do not have the same general metrical type for
all lines (such as poems that mix binary and ternary meters, or that mix types
of binary or types of ternary meter).[3]
We assume that the predominant or ambient meter in a poem must be one of the
following: iamb (O X, where “O” represents a weak, or metrically unstressed,
syllable and “X” represents a strong, or metrically stressed, one), trochee (X
O), anapest (O O X), amphibrach (O X O), or dactyl (X O O). We use the terms
predominant and ambient
equivalently to refer to the general metrical cadence of the poem, but,
crucially, not every strong position will be stressed linguistically in
recitation and not every weak position will be unstressed.[4] We identify pyrrhic (O O) and spondee (X X) as possible
idiosyncractic variants within generally iambic or trochaic poems, but we
exclude them a priori as a predominant type. We make no attempt to identify the
rarer ternary feet (those that do not have exactly one strong syllable) or any
feet that are neither binary nor ternary.
We can deal effectively with lines of different length where the differences
result from variation at the end of the line, such as catalexis (the omission of
unstressed syllables after the final stress) and hypermetricality (the
incorporation of additional unstressed syllables beyond the end of the final
foot), including hypermetrical syllables not only in the clausula (end of the
line), but also at the caesura (regular correspondence of a word boundary and a
foot boundary in the middle of the line). We can also recognize and deal with
irregularity at the beginning of the line, that is, with irregular anacrusis
(the inclusion of additional unstressed syllables before the initial
foot).
There is no principled way to distinguish whether, for example, a
nine-syllable line with stress on the even-numbered syllables should be
considered trochaic tetrameter with a regular anacrusis or iambic tetrameter
with a regular hypermetrical syllable in the clausula. In recognition of the
stronger general preference for iambic binary verse in Russian, we make that our
arbitrary identification when confronted with this sort of ambiguity in
situations where it cannot be resolved otherwise (e.g, on the basis of other
lines of the same poem that may have only eight syllables).
In Russian verse, as in English, the position of stress is not marked orthographically
and is not predictable from the orthography, which means that both the ambient meter
and
deviations from it cannot be identified purely from orthography (without additional
linguistic knowledge). The same is true of rhyme, which requires information about
both
the position of stress and the phonetic shape of the text, because the orthographic
system of the language is not purely phonetic. The first challenge, then, is to use
XML
tools to make explicit the stress and phonetic information that is only implicit in
poetic texts in natural orthography. Beyond that, as is illustrated below, if stress
and
pronunciation are to be made explicit within an XML model, the most natural
representations will involve mixed content. Mixed content poses special challenges
for
subsequent XML processing because although XSLT and XQuery (and the XPath function
library on which they depend) are well equipped to process sequences of nodes,
individual atomic values, and sequences of characters within strings, they were not
designed with core functions that support the processing of mixed content in a way
that
corresponds to a human reader’s understanding of continuous poetic text.
Research context
Our project design was developed partially to provide functionality that was not
prioritized in other existing computational treatments of verse, which were developed
with different goals. Two such examples involve the treatment of verse in the Text
Encoding Initiative (TEI P5) guidelines and in the poetry subcorpus of
the Russian National corpus (RNC poetry).
Text Encoding Initiative
The Text Encoding Initiative (TEI P5) recommendations concerning
metrical and rhyme information in verse texts are limited to using standoff metadata
to record analytic conclusions about the poetry.
The TEI recommendations are not designed to support tagging the actual textual
material so as to assist a computer in identifying
metrical and rhyme patterns. TEI verse markup features include:
(TEI P5, Section 6.4.1, cited 19 July 2015) Here
the @met and @rhyme attributes record the
meter and rhyme scheme in a summary, standoff way, but only after the
researcher has already determined them. There is nothing elsewhere in
the recommended TEI markup that explicitly associates the individual
minus, plus, and pipe characters in the @met attribute
value with specific moments in the text itself, and there is no markup
within the lines of the poem that would allow the automated computation
of those attribute values. This markup is intended not to support the
machine-assisted discovery of meter and
rhyme in XML-encoded poetic text files, but to record those properties only as user-supplied metadata
about the text.
The TEI recommendations suggest using the @met attribute
for the dominant, or ambient, meter in the poem and a @real
attribute on the level of individual lines to represent deviations from
that norm. Here, too, the TEI records only information the researcher
has already discovered, and provides no support for conducting
machine-assisted analysis that would identify and
compute these properties on the basis of an input corpus
where they have not previously been determined.
The TEI provides a rich <metDecl> element for
representing complex metrical patterns (TEI P5, Section
6.4.3, cited 19 July 2015), but here, too, what the markup records is
the researchers’ conclusions after analyzing the text. The
<metDecl> element is not used to mark up the text
itself in a way that would allow those patterns to be discovered with machine assistance.
Representing known metrical and rhyme schemes in a consistent way has obvious
benefits over a lack of consistency within or across projects, but the TEI
recommendations do not seek to address the scalability challenges described above.
Our approach to identifying meter and rhyme in a poetic corpus might be compared to
the methods researchers would employ in building an index of first lines, a common
interface feature of poetic corpora. The TEI does not need to advise users about
building this type of index because the procedure is obvious on the basis of the TEI
markup: the first <l> descendant element of the poem in formal
structural terms corresponds to the first line of the poem in human terms. Our goal
is to implement similar functionality for formal verse features that do not lie as
close to the surface as identifying the first line of a poem; for example, if a line
of verse is in iambic tetrameter, we should be able to learn that through processing
the data itself. The results of our analysis might then be recorded with TEI markup,
since we can translate our identifications algorithmically into the sorts of string
values expected for the TEI @met and @real and
@rhyme attributes. One of our goals, then, is to make metrical and
rhyme information accessible in a way that is closer to the actual textual content
than what the TEI provides through metadata attributes.
As noted above, our method is effective with Russian verse of a particular type,
but it is not easily transferred and applied to other national poetic traditions,
nor to Russian poetry that is not syllabotonic in nature. Insofar as the TEI
guidelines are intended to be of broad, general use, they are not the place to look
for instructions about, for example, identifying meter or rhyme differently in
Russian and in English. Even given that constraint, though, it would nonetheless be
desirable to see guidelines from the TEI about how to make information about meter
and rhyme accessible in a digital text not merely by assertion at the level of line
or stanza or canto or poem, but through computation at a lower textual level. The
TEI’s exclusive attention to standoff representations of meter and rhyme obscures
the fact that those emergent properties are derived substantially (if not entirely)
from phonetic (including stress) properties of the text. Our system therefore
prioritizes using those lower-level phonetic properties to derive the higher-level
poetic ones, which may then the recorded according to the TEI recommendations or
otherwise.
Russian National Corpus
The Russian National Corpus poetry subcorpus (RNC poetry) supports the
rendering of verse with accent marks, as in the following screen capture:
Figure 1: RNC stress example: Russian National Corpus output
Note the multiple stress marks on the last word of line 2, the second word
of line 3, and the middle word of line 4. In fact, these Russian words have
only a single stress.
What the Russian National Corpus represents with grave accent marks is not the
place of stress during recitation, but the location of strong metrical positions,
that is, places where stress might be expected according to to the ambient iambic
meter of this poem. This is useful information, although the online presentation may
be confusing because it uses a stress mark to identify not stressed syllables, but
strong metrical positions, only some of which correspond to linguistic stress. This
representation, which essentially just lays a general iambic tetrameter metrical
template on top of the words without regard to which syllables are or are not
pronounced with stress, makes it impossible to identify deviations from the ambient
meter. These deviations lie at the core of, for example, Kiril Taranovski’s (Taranovski 1953) law of regressive accentual dissimilation, discussed
below, and it is important for our research to be able to identify them.
Making stress explicit
The machine-assisted identification of meter and rhyme on the basis of plain-text
input documents in standard Russian orthography begins by tokenizing the text at white
space, normalizing it (stripping punctuation and folding case), and looking up each
word
form in a full-text dictionary. The dictionary is an XML database stored in eXist-db, with records that have the following form (in this example,
карий = brown (eye color)):
Each lexeme is an <item> element and every inflected form of the
lexeme is an item/form/content descendant, which encodes the place of
stress by wrapping the stressed vowel in <stress> tags. The only
content of the dictionary we use at present is the <content>
elements; for every word token in the input document (represented in the following
example as a $currentToken variable) we query the dictionary for
//content[. eq $currentToken]
which atomizes the
<content> element, effectively stripping any internal
<stress> markup and and comparing the string value to the plain
text input token. We retrieve the entire <content> elements that
match, including the internal <stress> elements, and transform them
with XSLT to wrap each match in <str> (= stressed
form) tags and to tag all vowel letters as <vowel> elements
with a @stress attribute that has three possible values: 1 =
stressed, -1 = unstressed, and
0 = stress status unknown.
In most cases the wordform is in the dictionary and there is a single match, which
gives us the unambiguous stress position for that token. If the word is not in the
dictionary or if there is an ambiguity (there are Russian wordforms that differ only
in
the place of stress, and that therefore are indistinguishable from one another in
plain
text), the dictionary returns all possible values. In the example above, the dictionary
will return
reflecting that there is only one match in the dictionary, and that the first of the
two
vowels in the wordform is stressed and the second is unstressed. A wordform not in
the
dictionary will be returned with @stress values of 0 for all
vowels. An ambiguity, such as города (an inflected form
of the lexeme городcity) will return two results:
with stress on the last vowel in the nominative and accusative plural). These results
are merged in postprocessing to return a single composite result:
reflecting that the stress properties of the first and third vowels are uncertain,
but
the middle vowel is unambiguously unstressed. Once the stress possibilities for each
token have been determined, it is reunited with its original orthographic representation
in the poem (before case-folding and the stripping of punctuation) and the text of
the
poem is returned with the following structure:
<poem>
<stanza>
<line>
<word>
<orth>карий</orth>
<str>к<vowel stress="1">а</vowel>р<vowel stress="-1">и</vowel>й</str>
</word>
<!-- other words -->
</line>
<!-- other lines -->
</stanza>
<!-- other stanzas -->
</poem>
So how, given the presence of 0 values in the dictionary lookup
results, do we do the following:
Determine the strong and weak positions in the poem, that is, the syllablic
positions in each line that tend toward being stressed and the ones that tend
toward being unstressed?
Determine whether the poem observes binary or ternary meter?
Determine, on the basis of the preceding, whether the binary meter is iambic
or trochaic and whether the ternary meter is dactylic, anapest, or
amphibrach?
Identify deviations from the ambient meter in individual lines?
Recognize and accommodate catalexis and hypermetrical syllables, the latter
both at the end of the line and before a caesura?
Metrical valence
The dictionary report can be understood as a matrix of 1,
-1, and 0 values, where the rows are lines of the poem
and the columns are vowel positions (<vowel> elements) in the line
(ignoring all textual content). For example, an eight-line passage of iambic tetrameter
without catalexis or hypermetrical syllables will be represented by an 8 x 8 matrix.
Exploiting our strategy of targeting poetry that exhibits a strongly regular
syllabotonic organization, for each column in the matrix we calculate whether the
position should be considered metrically strong or weak (provisionally; this initial
evaluation is modified subsequently, as described below) by counting the number of
1 values and dividing that count by the sum of the counts of
1 and -1 values (ignoring 0 values). We
call this the metrical valence of the position, and it varies
between a low of 0 (all unambiguous syllables in that position are unstressed) and
a
high of 1 (all unambiguous syllables in that position are stressed). If there are
no
unambiguous syllables in that position, we provisionally return a metrical valence
of 0
(identical to the valence of a weak syllable). These values are calculated easily
in
XQuery, e.g.:
let $lines := $poem/descendant::line
let $maxVowels := max($lines/count(descendant::vowel))
let $valences :=
for $position in 1 to $maxVowels
let $positive := count($lines[descendant::vowel[position() eq $position][@stress eq '1']])
let $negative := count($lines[descendant::vowel[position() eq $position][@stress eq '-1']])
let $valence :=
if ($positive + $negative eq 0) then 0 else $positive div ($positive + $negative)
return string($valence)
return string-join($valences,' ')
The string() and string-join() functions are for human
legibility; for machine processing the $valences variable can be returned
as a sequence of doubles that range between 0 and 1. The following first four lines
of
Aleksandr Puškin’s “Prorok” (“The prophet”):[5]
Even generally regular and consistent syllabotonic poetry contains frequent
divergences from the general metrical type, so that, for example, in iambic tetrameter
verse some even-numbered syllables may be unstressed and some odd-numbered syllables
may
be stressed. As was shown most comprehensively by Taranovski 1953, the
only absolute regularity is that the last metrically strong position (expected stress)
of every line must be linguistically stressed; other metrically strong positions may
be
linguistically unstressed.[6] For that reason, we do not expect that all values will be exactly equal to
either 0 (weak) or 1 (strong). To identify the strong and weak positions in a poem,
we
compare the metrical valence of each position in the matrix to its neighbors; positions
with a higher valence than both neighors are presumed to be strong (which we represent
as 1 and all others a presumed to be weak (which we represent as 0). This yields a
vector of 1 and 0 values, which we must then reconcile with one of the five basic
metrical line types (iambic, trochaic, anapest, amphibrach, dactylic) known in Russian
verse.
Identifying metrical types
Taking the preceding sequence of 0 and 1 values as input, we calculate the percentage
of matching values at a distance of two and at a distance of three, which we call
the
binary (resp. ternary)
coefficient of the poem. We predict that there will be no ties
in poetry that observes a reasonably regular and consistent syllabotonic structure.[7] To determine these coefficients, we start two (resp. three) syllables into
the sequence and proceed item by item, calculating the absolute value of subtracting
from each item in the sequence the one located two (resp. three) syllables before
it. We
then divide the sum of those values by the total number of sequences examined, returning
the mean. For example, with the following unambiguous iambic tetrameter pattern:
0 1 0 1 0 1 0 1
the binary coefficient is determined by subtracting from the 0 at position 3 the 0
at
position 1, then from the 1 at position 4 the 1 at position 2, etc. We sum the absolute
values of those subtractions (all of which equal 0 in this example) and divide by
the
number of comparisons (6 in this example), so the binary coefficient of this line
is 0.
The ternary coefficient starts at position 4 and subtracts from it the value three
positions before it, at position 1, and then proceeds until the end of the line,
similarly to the binary calculation. The absolute values in this case are all equal
to 1
and there are 5 comparisons, so the ternary coefficient is 5/5, or 1. Our system regards
the lower of the two values as determinative, so we report
correctly that this poem observes binary meter.
A perfect ternary example is similarly clear:
0 1 0 0 1 0 0 1 0 0 1 0
The binary coefficient of the preceding perfect amphibrach tetrameter is 6/10 or 0.60.
The ternary coefficient is 0/9, or 0, which identifies the type as ternary.
This value is easily determined with XQuery similar to the following (with
$valences set, for this illustrative example, to the values for the
Puškin poem):
let $valences := (0, 0.5, 0, 1, 0, 0, 0, 1, 0)
let $positions := count($valences)
let $binaries :=
for $position in 3 to $positions
return abs($valences[$position] - $valences[$position - 2])
let $binary := sum($binaries)
let $ternaries :=
for $position in 4 to $positions
return abs($valences[$position] - $valences[$position - 3])
let $ternary := sum($ternaries)
return
(concat('binary = ', $binary,'
'),
concat('ternary = ',$ternary,'
'),
concat('meter is: ', if ($binary lt $ternary) then 'binary' else if ($ternary lt $binary) then 'ternary' else 'tied'))
The output of the preceding is:
binary = 2.5
ternary = 3.5
meter is: binary
which
correct identifies the metrical type as binary. The output has been formatted for
human
legibility; what is important is that binary and ternary coefficients are doubles
that
can be compared, with the lower value determining the basic metrical type.
Once we have determined whether the basic type is binary or ternary, we can set all
positions to 0 or 1, which we consider a representation of the ambient meter, that is, the distribution of strong and weak positions
irrespective of whether they are filled by linguistically stressed or unstressed vowels.[8] We can then determine the subtype (iamb vs trochee for binary, anapest vs
amphibrach vs dactyl for ternary) by identifying the rightmost strong position in
the
line. Exploiting the fact that the final strong position in Russian syllabotonic verse
is the only one that must be stressed 100% of the time, we can distinguish among the
binary resp. ternary meters by counting the syllables (i.e., vowels) before the final
strong position. In binary meter, the final strong position is an odd-numbered syllable
in trochaic verse and an even-numbered syllable in iambic verse. In ternary meter,
the
final strong position is evenly divisible by 3 in anapest verse, divisible by 3 with
a
remainder of 2 in amphibrach verse, and divisible by 3 with a remainder of 1 in dactylic
verse. Since the final strong position in the Puškin example is the eighth syllable,
we
know that we are dealing with iambic verse, and specifically with iambic tetrameter.
We
can calculate this in XQuery (plugging in the values for the Puškin poem after resetting
them all to 0 or 1, alternating as appropriate for binary meter) along the lines of[9]:
let $valences := (0, 1, 0, 1, 0, 1, 0, 1, 0)
let $rightmostStrong := index-of($valences,1)[last()]
return if ($rightmostStrong mod 2 eq 0) then 'iambic' else 'trochaic'
Finally, the number of feet per line can be determined by counting the number of
strong position per line. XQuery such as the following can identify the number of
feet
(here preloaded with the values from the Puškin sample):
let $valences := (0, 1, 0, 1, 0, 1, 0, 1, 0)
let $countStrong := count($valences[. eq 1])
return
switch($countStrong)
case 1 return 'monometer'
case 2 return 'dimeter'
case 3 return 'trimeter'
case 4 return 'tetrameter'
case 5 return 'pentameter'
case 6 return 'hexameter'
case 7 return 'heptameter'
case 8 return 'octameter'
case 9 return 'nonameter'
case 10 return 'decameter'
default return 'other'
This correctly identifies the sample as tetrameter.
Overlapping hierarchies and the analysis of poetic texts
Poetic texts are often included among the poster children of overlapping hierarchies,
since the organization of poems into cantos, stanzas, lines, and feet is largely
independent of the sentences and words of the text. Yet although foot boundaries and
word boundaries are mutually independent, the implementation of caesura depends on
their
synchronization. Overlap is a well-understood challenge for XML systems because the
XML
data model regards documents as rooted directed acyclic graphs with single
parenthood and a total ordering on leaf node (Marcoux, Sperberg-McQueen, and Huitfeldt 2013), which requires that every element
be fully nested within all of its ancestors. XML-compatible strategies for representing
overlapping reality are also well understood, and rely, whether implicitly or
explicitly, on prioritizing at most one hierarchy syntactically and flattening any
competing hierarchies by delimiting their conceptual elements as paired milestone
tags
or standoff pointers. And as the implementation of LMNL tools using XML technologies
has
shown, the XML toolkit is sufficient flexible to support a view of documents as
overlapping ranges, where hierarchy is not a fundamental organizational aspect of
the
data model. (Piez 2012, Piez 2014)
The present project is not using (and therefore not tagging) linguistic units other
than words, so the inherent overlap relationship between some aspects of verse structure
(e.g., cantos, stanzas, and lines) and some aspects of linguistic structure (e.g.,
sentences) is not an issue. But metrical foot boundaries and word boundaries commonly
exhibit an overlap relationship, and, as noted above, that relationship is important
for
identifying caesura. A caesura is a regular coincidence of foot and word boundaries,
as
in the following initial quatrain of Zinaida Gippius’s 1907 Neljubov′
(Non-love):
Caesura may be understood as the regular coincidence of metrical and word boundaries
at the same position in multiple lines in a poem, where the regularity creates an
expectation of potential pause in the ear of the listener.[12] The preceding quatrain (and the entire sixteen-line poem) is written in
strict iambic tetrameter with a hypermetrical caesura in all lines and a hypermetrical
clausula in the odd-numbered lines. The second column depicts the metrical structure:
stressed syllables are represented by an X, unstressed syllables by an
O, hypermetrical syllables are parenthesized, the boundary between
feet is marked with a pipe, and the caesura with a double pipe. The third column marks
the stressed and unstressed syllables and hypermetricality the same way, with
X and O and parentheses, respectively, but here the
pipe demarcates not metrical feet, but phonological words (the double pipe continues
to
represent the caesura). In the first column the foot boundaries are represented by
pipes
(mapped from the second column onto the first); word boundaries are represented by
white
space, except that here the white space has its normal orthographic property of
delimiting orthographic, rather than phonological words.
Comparison of these three parallel columnar views of properties of the same underlying
data shows that the boundaries between metrical feet and phonological words frequently
fail to coincide, but the transition between the fifth and sixth syllables of every
line
regularly separates both metrical feet and phonological words—that is, it represents
a
regular caesura. This synchronization of the overlapping metrical and
phonological-lexical hierarchies is the essence of poetic caesura, and our challenge
is
to identify it automatically, so that our analysis can be scaled to a poetic corpus
so
large that manual individual analysis of all poems would be impractical.
A caesura is a regular phenomenon that becomes
perceptible when and because it occurs throughout a
poem. Or almost throughout a poem: Gippius plays with
this expectation in her 1905 Ona (She), a sixteen-line
poem with a regular caesura except in the pivotal eighth line, at the end of the first
half of the poem. And she similarly plays with the hypermetricality of the dactylic
caesura on the second line of the poem, where the sixth syllable of this line, and
only
of this line, is most naturally read with a stress:
The identification of caesura requires the identification of both feet and words,
which are not coextensive and which frequently overlap. The challenge, then, is to
locate where foot and line boundaries coincide without employing markup in a way that
would violate well-formedness overlap constraints.
Explicit and implicit markup
The markup community typically uses the term markup
to refer to the insertion of (angle-delimited in XML) tags into a stream of text as
a
way of making explicit information about its structure, semantics, or other properties.[14] It is also well known, however, that so-called plain
text is more than a sequence of informationally equivalent content units,
and that some characters may represent data content while others may represent
components of structure. As TEI P5 notes, A text is not an
undifferentiated sequence of words, much less of bytes. In some cases the
same information may be represented in plain text by raw characters (e.g., quotation
marks to delimit quotations, space characters to delimit words, new line characters
to
delimit lines of poetry, multiple new line characters to delimit paragraphs of prose,
asterisks or underscores to delimit emphasized text, etc.) and in XML by markup. The
richness vs sparseness of a markup schema, at least in digital humanities projects,
typically represents a compromise between making structure and semantics explicit
through the use of markup, on the one hand, and not letting the markup overwhelm the
content in a way that compromises human legibility, on the other.[15] Furthermore, richer markup increases the risk of overlap, which is
prohibited syntactically in XML. For example, if in an XML representation of a poem
we
try to tag explicitly both metrical feet and words, the two hierarchies will typically
overlap because they are largely independent of each other structurally. But can we
use
a combination of explicit and implicit markup to represent the logically overlapping word and foot hierarchies needed for caesura
without violating the XML well-formedness constraint against syntactically overlapping tags?
Identifying elements without identifying their boundaries
In our use of the dictionary to identify lexical stress we tokenize the plain text
input on white space and pass each word into the dictionary web service, which
returns it with <word> and <str> wrapper tags.
This establishes <word> elements as constituting the fully
tesselated element content of <line> elements. Independently of
word tagging, the procedure described above to identify the strong and weak vowel
positions and the stressed and unstressed vowels in the text enables us to assign
every <vowel> element to a particular foot on the basis of its
position in the sequence of <vowel> descendants of the
<line>, but 1) where do the actual foot boundaries fall and
2) how do we represent them in the XML, given the prohibition against encoding fully
tesselated <foot> elements that would overlap with our fully
tesselated <word> hierarchy?
It turns out, perhaps surprisingly, that we don’t need to care about the answers
to those questions; it is sufficient for the purpose of locating caesura to identify
only the two (resp. three) syllabic nuclei (vowels) of binary (resp. ternary) feet,
without locating the precise boundaries of the syllables or the feet and without
tagging them. Once we determine that, for example, a poem is written in iambic
tetrameter, we know that there are logical milestone foot boundaries somewhere between a strong vocalic position
(even-numbered <vowel> element, in the sequence of
<vowel> descendants of the <line>) to the
left and a weak vocalic position (odd-numbered <vowel> element)
immediately to its right (subject to adjustment for catalexis and hypermetricality),
and that is all that we need to know. If in two adjacent words
(<word> elements) the last vowel (<vowel>)
of the word to the left is in an even-numbered position and the first vowel of the
word to the right is in an odd-numbered position, we can infer that the foot
boundary coincides with the word boundary, which is already present explicitly in
our markup. Otherwise, we can infer that the foot boundary does not coincide with
a
word boundary, and in that case we do not need to care about its exact position. If
in every line of this hypothetical iambic tetrameter poem, then, the fourth
<vowel> descendant of each <line> element
is the final <vowel> in its containing <word>
element, we have a regular caesura.[16]
The insight here, then, is that we can privilege (fully mark up) the word-level
hierarchy and allow the position of <vowel> elements inside
containing <word> and <line> elements to
supply enough information to identify the presence or absence of caesura without our
needing to know anything more about syllable or foot boundaries in the poem. The
full system needs to adapt to lines of different length (including those with an odd
number of feet, where the simple arithmetic division below is not appropriate) and
needs to account for hypermetrical caesura, but the simplified XQuery below
illustrates the general strategy for locating caesura by targeting iambic tetrameter
without hypermetrical caesura (although with optional hypermetrical clausula) that
has been tagged for words but not for syllables or feet:[17]
let $vowelCount := min(//line/count(descendant::vowel))
let $midPoint := $vowelCount div 2
let $targets := //line/descendant::vowel[position() eq $midPoint]
return
if ($targets/following-sibling::vowel)
then
'no caesura'
else
'caesura'
Tokenizing mixed content
Above we illustrate how to identify the foot boundaries we care about for the
purpose of identifying caesura without having to tag syllables or feet, while
relying on the presence of <word> tags. In this project the
dictionary web service happens to tag words as a side-effect of locating the place
of stress, but what if the <word> tags were not there? For
example, how would we deal with input like:
To a scholar of poetry, the task appears to involve separating words on white space
when they happen to have stresses marked, but the XPath and XSLT resources that
support tokenization (XPath tokenize() and XSLT
<xsl:analyze-string>) atomize their contents, which means
that neither can be used directly on this content to achieve this end. This mismatch
between the human understanding of line (a unit of poetry) and the
XML understanding of <line> (an XML element) results from the
fact that humans naturally perceive the line as something that can be regarded as
words separated by white space, something that XML processing is unable to do
without atomization.[18]
The strategies for dealing with this mismatch between human and XML perspectives
are well known to XSLT programmers, although perhaps not intuitive to human readers
of poetry: either 1) convert the markup (in this case, <stress>
tags) to plain-text characters that do not otherwise occur, tokenize the resulting
plain text, and then convert the plain-text characters back to markup or 2) convert
the relevant plain-text characters to markup. The latter can be illustrated with the
following sample:
<poem>
<quatrain>
<line>"Мой д<stress>я</stress>дя с<stress>а</stress>мых ч<stress>е</stress>стных пр<stress>а</stress>вил</line>
<line>Когд<stress>а</stress> не в ш<stress>у</stress>тку занем<stress></stress>ог,</line>
<line>Он уваж<stress>а</stress>ть себ<stress>я</stress> заст<stress>а</stress>вил</line>
<line>И л<stress>у</stress>чше в<stress>ы</stress>думать не м<stress>о</stress>г.</line>
</quatrain>
</poem>
using a two-pass XSLT approach. First convert space characters in
text() node children of <line> to empty
milestone elements (such as <word/>):
The dictionary stress lookup obviates the need to tokenize mixed content for our
project, which means that the location of stressed vowels within words can be
encoded explicitly with markup, even though the location of those vowels within feet
is not encoded explicitly. The example above illustrates that this type of analysis
would also be possible even without explicit <word> tags in the
input markup. Although it happens not to be needed within our work flow, this type
of tokenization task is nonetheless common in digital humanities, at times in
situations where it would be impossible to tag words without creating overlapping
markup. For example, in Povestʹ vremennyx let it is necessary to tokenize not only
lines like:
<Aka>рѹ<sup>с</sup><problem>каꙗ</problem><lb/> землѧ кто в неи поча<sup>л</sup> первое кнѧжити.</Aka>
where the <sup> (superscription) tags are comparable to
<stress> or <vowel> tags at different
stages in our poetry project. In that project it is also necessary to flatten
elements that span multiple words to avoid stranding a start tag inside one
<word> element and its corresponding end tag inside another,
as in
Here the <marginalia> start and end tags would have to be
tokenized with different words, which can be done by flattening them into
milestones, with or without Trojan pointers,[20] depending on whether the elements need to be re-erected and on whether
there might be problems with ambiguous associations involved in their
restoration.
No discussion of overlap would be complete without a nostalgic nod in the
direction of the optional CONCUR feature of SGML (Goldfarb 1990
177), but the ways in which SGML anticipated some of the issues discussed here go
beyond CONCUR. Because SGML was concerned with simplifying the markup process by
reducing typing, it allowed the developer to specify, as part of the SGML
declaration, several types of markup minimization. Our decision to treat space
characters as if they were milestone tags in the examples above gives us access to
an additional hierarchy at processing time without actually writing markup into the
document. That meaning of space characters is inherent in the popular, vernacular
understanding of spaces as delimiting words in normal Russian orthography, but from
an XML perspective a space is just a character, like any other, in a
text() node, and its interpretation as markup becomes part of the
processing model only when we choose to treat it as such during querying or
transformation. SGML, though, provides, through a combination of OMITTAG and
SHORTREF, the ability to create a map that encodes
the milestone function of space characters in the DTD, which is an obligatory part
of the SGML document, and therefore at the level of the document model itself, prior
to transformation or other processing (Goldfarb 1990 429–32, DeRose 1997 210). In this way SGML formalizes, as part of the
document syntax, a structure that in our XML implementation is only a human
interpretive convention at the document level, and that becomes part of the formal
structure only when processed in a way that applies that interpretation.
Mixed content as a type of overlap
The XML literature conceptualizes overlap in several ways; see, e.g., the overviews
in
DeRose 2004, Bauman 2005, the many articles listed
at Balisage Concurrent markup/overlap, and, from a TEI perspective, Bański 2010. None of these, though, mentions that mixed content of the
type described above, where words boundaries are represented by space characters,
rather
than by tags and without standoff pointers, is also an intellectual manifestation
of
overlap. This type of situation has not previously been identified as overlap because
from a syntactic perspective it isn’t, and the tesselated hierarchy instantiated by
the
space characters does not raise errors or compromise well-formedness in the presence
of
other (explicit) markup because from a strict XML perspective it is implicit, rather than overt. As is shown above, though, it
may nonetheless raise processing challenges comparable to those that arise with other
non-overlapping overlap strategies, such as Trojan milestones or the
other syntactically licit expressions of overlap described in DeRose 2004 and Bauman 2005. In this example, white space characters are
milestones tags in disguise, and milestones of this type are surrogates for wrapper
tags, with the result that the space characters implement a tesselated hierarchy that
may overlap with others in ways that become overt only when it is necessary to address
that hierarchy explicitly during processing.
Mixed content and rhyme
The present report concentrates on the identification and analysis of meter, but some
of the mixed-content issues mentioned above are also relevant for the identification
and
analysis of rhyme. The requirements for rhyme in Russian are close to those in English,
and in both languages rhyme must be identified according to pronunciation, rather
than orthography.[21] In Russian, however, it is possible to convert from orthography to a broad
phonetic trancription algorithmically, with negligible need for lookup dictionary
queries, as long as the place of stress is known. Since our system already enriches
the
orthographic representation of the text with information about stress as part of the
process of determining meter, we can also leverage that information to identify rhyme.[22] An algorithm for mapping between Russian orthographic (enhanced by stress
markup) and broad phonetic representations is given in Adams and Birnbaum 1997, and the principal challenges to regarding
orthography as a direct surrogate for phonetics are that:
Most vowel letters are pronounced differently under stress than they are
when they are not stressed. One effect of this process, commonly called
vowel reduction, is that two letters
may have different pronunciations when they are under stress but the same
pronunciation when they are not stressed.
As noted above, certain small Russian orthographic words (prepositions,
the negative particle не, and a few others)
function as clitics, merging with adjacent head words to form a single
phonological word, or stress group. This means that some properties of
Russian pronunciation require looking beyond the domain of the orthographic
word, although these larger domains are not part of the markup of the input
text that is implied by white space.
Consonants may undergo assimilative changes in voicing and palatalization
in certain environments. These assimilations may cross word boundaries,
which means that they may involve consonants that might be understood by a
human reader as phonetically adjacent even though they are not contiguous
(or even siblings) from an XML perspective. For example, the type of regular
expression processing one might use to say pronounce т (phonetic [t]) as д (phonetic [d]) before д (a voicing assimilation) becomes more than a
simple regular expression pattern when the first consonant is at the end of
one <word> element and the second at the beginning of the
next.
Several Russian letters (soft vowel letters, soft sign)
have a diacritic function whereby they represent properties of preceding
consonant letters. For example, in the Russian sequences та and тя, the
initial consonant letters are the same and
the final vowel letters are different. But
the division of the sequence into segments phonetically differs from the
division into letters: phonetically these syllables have different initial
consonant sounds and the same final vowel
sounds (see Adams and Birnbaum 1997 for discussion). Because vowels are all
tagged as <vowel> elements in our representation and
consonants are in text() node siblings of the
<vowel> elements, here, as with some instances of
consonant assimilation, the segments are adjacent from a human perspective
but not from an XML perspective. Vowel reduction, introduced in the first
point above, is also sensitive to consonants that are adjacent to the
unstressed vowels from a human perspective, but not from an XML
perspective.
We cannot analyze rhyme without access to information about the place of stress
because, among other things, end rhyme in Russian verse is defined as a phonetic match
that begins at the final stressed vowel and continues through the end of the line.
We
can, however, convert each line algorithmically to a plain text phonetic representation
and then perform regular expression matching on the outputs of that conversion function.[23] For example, since our phonetic representation makes no other use of
upper-case vowel letters, we can use those[24] to represent stressed vowels. We can then identify lines as rhyming by using
a regular expression to isolate the rightmost upper-case vowel letter and everything
that follows it from each line and to compare them with one another.[25]
At this stage of the project we identify only perfect rhyme, where all sounds in the
rhyme domain correspond. We anticipate, though, extending this strategy to analyze
patterns of slant (inexact) rhyme, that is, to identify
whether a particular poet permits non-correspondence with respect to some phonetic
features, but not others. Much as we rely on the notion of ambient meter to infer
metrical consistency even where individual lines may deviate from it, so we might
identify an ambient rhyme scheme based on unambiguous
matches and extend that by inference to lines that deviate from it. We could then
decompose phonetic inexact rhymes into distinctive features to determine whether,
for
example, a particular poem or a particular poet employs imperfect rhyme only of specific
types, that is, by neutralizing specific phonetic distinctions but not others.
Conclusions
The strategies we employ to identify and analyze Russian meter and rhyme on the basis
of plain text input in normal Russian orthography are useful not only because of what
they tell us about Russian verse (the digital humanities questions), but also because
of
what they illustrate about structured text (the markup questions). The methods we
employ
for working around overlap constraints are well known, but in particular:
We have long known that plain text typically uses certain charaters, such
as white space and punctuation, as a sort of pseudo-markup, and also that
the extent to which structural and other features of XML texts are made
explicit through tagging depends on the purposes and preferences of the
researcher. What we have tried to emphasize and illustrate above is that
conceptual overlap may exist not only where it has been discussed
intensively before (e.g., where milestones are used to flatten a hierarchy
or where standoff is employed), but also in at least two situations that
have not previously played a role in the general overlap discourse:
The boundaries of an overlapping hierarchy are not encoded at
all. This is illustrated by the identification of feet even when
foot boundaries are not only untagged, but also completely
unrepresented, and even unknown.
An overlapping hierarchy may be encoded through plain text
pseudo-markup, but the avoidance of overlap is only apparent,
and may vanish when the implicit hierarchy needs to be
processed. This is illustrated by delimiting word boundaries
with space characters, which is well known insofar as it is
common in digital humanities projects not to tag all individual
words explicitly. However, the consequences of needing
nonetheless to treat words during processing as though they had
been marked up individually has figured in discussions of
overlap largely as a technical complication to be overcome. What
has not played as significant a role in the discourse is the
fact that this use of white space with a milestone function is
ultimately an encoding of overlap.
Like other strategies for avoiding syntactic overlap, the methods
discussed here raise no well-formedness errors, but they may pose similar
processing challenges as soon as the researcher needs to engage with them
explicitly.
To a human reader of poetry, consonants, stressed vowels, and unstressed
vowels are all part of a continuous stream of phonetic material. Stressed
vowel sounds are phonetic units in that stream, just like unstressed vowel
sounds and consonant sounds; that is, they exist on the same (phonetic and
conceptual) level. Meanwhile, the XML representation of stressed vowels as
<stress> elements puts the stressed vowel letter
itself on a different level of the hierarchy than the rest of the letters
that represent the text of the poem.[26]
For these reasons, it is useful to recognize that both situations described above
represent a type of overlap that has not traditionally been identified and discussed
as
such explicitly.
[FEB] Fundamental′naja èlektronnaja biblioteka
Russkaja literatura i fol′klorhttp://www.feb-web.ru/ [cited 19 July 2015]
[Friedberg 2009] Friedberg, Nila. The
Russian Auden and the Russianness of Auden. In: Aroui, Jean-Louis, and A.
Arleo, ed. 2009. Towards a typology of poetic forms: From language
to metrics and beyond. Language faculty and beyond: Internal and external
variation in linguistics 2. Amsterdam; Philadelphia: John Benjamins Pub. Company.
229–45.
[Friedberg 2011] Friedberg, Nila. 2011.
English rhythms in Russian verse: On the experiment of Joseph
Brodsky. Trends in linguistics. Studies and monographs 232. Berlin:
Mouton de Gruyter.
[Goldfarb 1990] Goldfarb, Charles. 1990.
The SGML handbook. Oxford: Clarendon.
[Marcoux, Sperberg-McQueen, and Huitfeldt 2013] Marcoux, Yves, Michael
Sperberg-McQueen and Claus Huitfeldt. Modeling overlapping structures: Graphs and
serializability. Presented at Balisage: The Markup
Conference 2013, Montréal, Canada, August 6–9, 2013. In Proceedings of Balisage: The Markup Conference 2013. Balisage
Series on Markup Technologies, vol. 10 (2013). doi:https://doi.org/10.4242/BalisageVol10.Marcoux01.
[Piez 2012] Piez, Wendell. Luminescent:
Parsing LMNL by XSLT upconversion. Presented at Balisage: The Markup Conference 2012, Montréal, Canada, August 7–10,
2012. In Proceedings of Balisage: The Markup Conference
2012. Balisage Series on Markup Technologies, vol. 8 (2012). doi:https://doi.org/10.4242/BalisageVol8.Piez01.
[Piez 2014] Piez, Wendell. Hierarchies
within range space: From LMNL to OHCO. Presented at Balisage: The Markup Conference 2014, Washington, DC, August 5–8, 2014.
In Proceedings of Balisage: The Markup Conference 2014.
Balisage Series on Markup Technologies, vol. 13 (2014). doi:https://doi.org/10.4242/BalisageVol13.Piez01.
[Shaw 1993] Shaw, J. Thomas. 1993. Pushkin’s poetics of the unexpected: The nonrhymed lines in the rhymed
poetry and the rhymed lines in the nonrhymed poetry. Columbus, OH:
Slavica.
[Taranovski 1953] Taranovski, Kiril. 1953.
Ruski dvodelni ritmovi. Posebna izdanja, knjiga
CCXVII. Odelenije literature i jezika, knjiga 5. Belgrade: Srpska akademija nauka.
[Tsur 2012] Tsur, Reuven. Poetic rhythm: Structure and performance. An empirical study in cognitive
poetics. Second edition, revised. Eastbourne, UK and Portland, OR: Sussex
Academic Press.
[2] For comparative information about English on this and other points,
see the challenges faced by the Stanford Literary Lab in their automated
analysis of English verse, discussed in Trans-historical Poetry Project.
[3] We can illustrate the strong syllabotonic organization of Russian
verse practice and the more tonic organization of contemporaneous
English verse practice by comparing Robert Southey’s (1774–1842) “The
old woman of Berkeley” (1799) with Vasilij Andreevič Žukovskij’s
(1783–1852) 1814 (first published in 1831) translation of Southey’s
text. Here are the first four lines of Southey’s original:
Table I
Text
Syllables per foot
Rhyme
The ra|ven croaked | as she sate | at her meal,
2 2 3 3
a
And the Old | Woman knew | what he said;
3 3 3
b
And she | grew pale | at the Ra|ven’s tale,
2 2 3 2
c
And sick|ened, and went | to her bed.
2 3 3
b
And here is Žukovskij’s translation:
Table II
Text
Syllables per foot
Rhyme
На кро|вле во|рон ди|ко про|кричал —
2 2 2 2 2
a
Стару|шка слы|шит и | бледнеет.
2 2 2 2+
B
Понят|но ей, | что во|рон тот | сказал:
2 2 2 2 2
a
Слегла | в постель, | дрожит, | хладеет.
2 2 2 2+
B
The numbers in the second column record the number of syllables
per foot; note that Southey’s vary between 2 and 3, while Žukovskij’s
are consistently 2 (the plus sign means that there is a hypermetrical
unstressed syllable at the end of the last foot of the line). The
letters in the third column record the rhyme scheme; note that Southey
rhymes only the even-numbered lines, while Žukovskij more densely rhymes
both the odd-numbered and the even-numbered lines. This example is
typical of the stronger formal regularity in Russian verse with respect
to both meter and rhyme than in contemporaneous English verse, and it
illustrates why our methods are effective with Russian verse but cannot
be transferred and applied easily to the English tradition.
We are grateful to Elisa Beshero-Bondar for bringing this example to
our attention, and for her many insightful observations about Southey’s
ballad practice.
[4] Metrical variation, that is, deviation from the predominant meter, is
used by poets for a variety of reasons and purposes. It preserves meter,
while preventing poetry from becoming “sing-song”; it establishes
associations among words and lines; it modulates the tempo; it draws
attention to important moments; and it adapts international meter to
local linguistic properties (e.g., stress system, word length). For
example, English has a lot of monosyllables and Russian has a lot of
words with three or more syllables, which means that neither language is
a perfect fit for completely regular binary or ternary meter. Metrical
variation allows the predominant meter to emerge clearly without
undermining the pronunciation of the poetic text in ways that are
generally consistent with natural speech rhythms.
Scholars of versification sometimes refer to the syllables where we
expect stress according to the ambient metrical cadence as
strong (vs. weak) and to
syllables that are pronunced more emphatically than their neighbors as
stressed (vs. unstressed).
Metrical variation means that not all strong syllables are stressed and
not all weak syllables are unstressed, but linguistic stress and
metrically strong position nonetheless coincide with sufficient
consistency to impart a clear and regular general rhythm to the poetic
text. Part of the challenge of machine-assisted metrical analysis is
dealing with both this regularity and the deviations from it.
Some scholars (e.g., Friedberg 2009, adopting
terminology advanced in the 1970s by Morris Halle and Paul Kiparsky)
employ the term meter for what we call
ambient or dominant meter and rhythm for what we refer to as the actual line-level meter.
[5] As Friedberg 2011 explains, many monosyllables in Russian,
including the pronominal forms я (1sg personal
pronoun, nominative case, line 2) and мне (1sg
personal pronoun, dative case, line 4), are read with stress in strong metrical
position and without stress in weak metrical position. The dictionary returns
these as stressed (1), but we reset them (based on a lookup list
of words subject to this treatment) to 0 (ambiguous stress)
before calculating the metrical valence.
[6] The probability that a metrically strong position will be occupied by a vowel
that carries linguistic stress follows an alternating pattern that has comes to
be known as the law of regressive accentual dissimilation.
According to this principle, the penultimate strong position is the one that is
most likely to be filled with an unstressed vowel, the antepenult is the one
most likely to be stressed after the last, etc. For Puškin’s
Prorok, the frequency with which linguistic stress actually
coincides with specific syllabic positions in the line may be illustrated as
follows:
Figure 2: Aleksandr Sergeevič Puškin, Prorok
This regularity of Russian verse applies throughout the history of the
syllabotonic tradition, although with variation over time. It emerges naturally
from the confluence of several linguistic properties of Russian, and it is not
characteristic of English syllabotonic verse.
[7] Hypermetrical caesura must be identified and handled specially.
[8] As noted above, the pattern may be complicated by hypermetrical caesura, the
identification of which requires special handling.
[9] Here and in many other listings, we use XQuery or XSLT for legibility, but the
heavy lifting is being done by XPath, and the same functionality could be
implemented a single XPath expressions. In this case, for
example:
if (index-of((0, 1, 0, 1, 0, 1, 0, 1, 0),1)[last()] mod 2 eq 0) then 'iambic' else 'trochaic'
Like the damp wind you beat at the shutters,
Like the black wind you sing: You are mine!
I am primordial Chaos, I am your friend of old,
Your only friend, Open! Open!
[Our
translation]
[11] In Russian, phonological words (characterized by a single stress)
are not always coextensive with orthographic words (separated by
spaces), and the definition of word that matters for
versification is the phonological word, or stress group.
Specifically, Russian clitic particles, such as prepositions and the
negative particle ne (no;
not) form a single phonological word (characterized by a
single stress) with their head words. Additionally, the stress of
many categories of monosyllabic words that are not phonological
clitics in normal spoken Russian is determined in poetry by the
ambient meter, so that the words are pronounced with a stress during
reading when they fall in a metrically strong position (one where
stress is expected according to the ambient meter) and without a
stress in a metrically weak position (Friedberg 2011). For that reason, the boundaries in this column identify
transitions not between orthographic words, but between phonological
words, that is, stress groups.
[12] Whether there must be a pause during performance is a separate question. See
the review of the literature in chapter 4, Caesuraof Tsur 2012.
In her shameless and pathetic baseness,
Like dust, she is gray, like mortal remains.
And I am dying of this fatal closeness,
Because she cannot be dissolved from me.
She is rough and she is thorny;
She is cold—she is a snake.
I am wounded all over by her sickly-burning,
Elbow-shaped scales.
Oh, to feel her sharpened sting!
Sluggish, dull, and silent.
She’s such a grave and such a faded thing,
And there is no reaching her—she’s deaf.
Obstinately, with her coiling rings
She snuggles up to me, strangling me whole.
And this most dead, most black of things,
Most terrifying she—she is my soul!
[14] Standoff markup may also be used for this purpose. In both cases the markup is
distinguished syntactically from the textual content.
[15] In some cases markup may be used to add information that is not otherwise
easily accessible in plain text; common cases in a digital humanities context
include named entity and anaphor identification or disambiguation. In other
cases markup that duplicates information that is accessible in plain text is
nonetheless used for ease in retrieval and analysis; a common case in a digital
humanities context might involve tagging words that are otherwise fully
delimited by white space. In other instances markup attributes might be used to
duplicate information already encoded through element structure; a common case
involves numbering acts or scenes in a play or chapters in a novel when the
section or division hierarchy already fully encodes and represents that ordering
information in an alternative way.
Human legibility of tagged source files is not a requirement for all digital
text projects, but it is a factor that digital humanists often consider when
designing their markup schemas.
[16] Identifying the syllable boundaries needed to locate foot boundaries
within an orthographic word is complex because in some cases variation is
possible. The pipe characters that represent foot boundaries within words in
the examples above were added by hand to simplify reading, and they are not
an integral part of our analysis. It is possible to identify syllable
boundaries in text in natural orthography algorithmically if we are willing
to make arbitrary decisions in situations that permit variation, but because
that level of precision is not relevant to our analysis, we have not
prioritized implementing that functionality at this time.
[17] As was noted above, the XQuery is for human legibility, and the actual
analysis can be expressed purely in XPath.
[18] The usual subordination of words to lines may be violated, often for the
comic effect that comes from the unexpected, as in Tom Lehrer’s
Smut:
As the judge remarked the day that he acquitted my Aunt Hortense,
“To be smut
It must be ut-
Terly without redeeming social importance.”
[19] We have not tagged unstressed vowels in order to preserve legibility, but
should that additional markup be needed, it is easily implemented with
<xsl:for-each-group> and a @regex
attribute value that matches vowel letters.
[21] The phonetics that correspond to the orthographic sequence ough
in English is notorious in this respect. See, for example, A
rough-coated, dough-faced, thoughtful ploughman strode through the streets
of Scarborough; after falling into a slough, he coughed and
hiccoughed., cited at
https://www.englishclub.com/ref/esl/Power_of_7/7_Ways_to_Say_ough__2924.htm
[cited 19 July 2015].
[22] The same is true for other phonetic patterning, such as more generalized
instances of consonance and assonance. The identification of line-internal rhyme
of some types might require attention to word boundaries, and we have not made
that a goal of the project at the present time.
[23] This two-step process (1. convert orthography to phonetics and then 2. compare
the phonetic representations) reenacts the human experience of seeing letters on
the printed page and recognizing rhyme on the basis of the sounds represented
(often not on a one-to-one basis) by those letters.
[24] Or any other Unicode characters that do not otherwise occur in the phonetic
surrogate of the line, but repurposing case is probably the most legible
option.
[25] Open masculine rhyme also requires that the supporting
consonants before the final stressed vowels match. This feature distinguishes
Russian from English rhyme; see and tree rhyme in
English, but the Russian equivalents would not rhyme because of the absence of a
supporting consonant.
[26] From the perspective of the continuous stream of sound, that
stressed vowel sounds are located on a lower level of the hierarchy
than other phonetic material is an XML side-effect. It is not
structurally different from many other inline markup situations
(e.g., emphasis), except perhaps in degree, in that stress tagging
almost always (except in the case of words consisting of a single
stressed vowel, and then only within the individual word) creates
mixed content.
The contradiction between the XML modeling and the human
understanding of poetic utterances is not obviated by encoding
stress as a textual diacritic or by encoding stressed vowels as
different textual characters than their unstressed counterparts,
which are possible syntactic alternatives to the markup employed
here. Each of those alternatives contradicts the human understanding
of the content of the line differently but not less than our use of
a <stress> wrapper element.
Adams, Lawrence
D. and David J. Birnbaum. 1997. Perspectives on computer programming for the
humanities. Originally published in Text
technology 7, 1: 1–17 with an updated version available at
http://poetry.obdurodon.org/reports/adams-birnbaum.xhtml. [cited 14
June 2015]
Friedberg, Nila. The
Russian Auden and the Russianness of Auden. In: Aroui, Jean-Louis, and A.
Arleo, ed. 2009. Towards a typology of poetic forms: From language
to metrics and beyond. Language faculty and beyond: Internal and external
variation in linguistics 2. Amsterdam; Philadelphia: John Benjamins Pub. Company.
229–45.
Friedberg, Nila. 2011.
English rhythms in Russian verse: On the experiment of Joseph
Brodsky. Trends in linguistics. Studies and monographs 232. Berlin:
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Gasparov, Mixail Leonovič.
1984. Očerk istorii russkogo stixa. Metrika, ritmika, rifma,
strofika. Moscow: Nauka. Selections available on line at
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2015]
Marcoux, Yves, Michael
Sperberg-McQueen and Claus Huitfeldt. Modeling overlapping structures: Graphs and
serializability. Presented at Balisage: The Markup
Conference 2013, Montréal, Canada, August 6–9, 2013. In Proceedings of Balisage: The Markup Conference 2013. Balisage
Series on Markup Technologies, vol. 10 (2013). doi:https://doi.org/10.4242/BalisageVol10.Marcoux01.
Piez, Wendell. Luminescent:
Parsing LMNL by XSLT upconversion. Presented at Balisage: The Markup Conference 2012, Montréal, Canada, August 7–10,
2012. In Proceedings of Balisage: The Markup Conference
2012. Balisage Series on Markup Technologies, vol. 8 (2012). doi:https://doi.org/10.4242/BalisageVol8.Piez01.
Piez, Wendell. Hierarchies
within range space: From LMNL to OHCO. Presented at Balisage: The Markup Conference 2014, Washington, DC, August 5–8, 2014.
In Proceedings of Balisage: The Markup Conference 2014.
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Shaw, J. Thomas. 1993. Pushkin’s poetics of the unexpected: The nonrhymed lines in the rhymed
poetry and the rhymed lines in the nonrhymed poetry. Columbus, OH:
Slavica.
Taranovski, Kiril. 1953.
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Trans-historical
Poetry Project. Developed at the Stanford University Literary Lab by Ryan Heuser,
Mark
Algee-Hewitt, Maria Kraxenberger, J. D. Porter, Jonny Sensenbaugh, and Justin Tackett.
https://github.com/quadrismegistus/litlab-poetry, and especially the
slides from their DH2014 Lausanne presentation at
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