No source, born digital.
Contributions to Contemporary History is one of the central Slovenian scientific historiographic journals, dedicated to publishing articles from the field of contemporary history (the 19th and 20th century).
The journal is published three times per year in Slovenian and in the following foreign languages: English, German, Serbian, Croatian, Bosnian, Italian, Slovak and Czech. The articles are all published with abstracts in English and Slovenian as well as summaries in English.
Prispevki za novejšo zgodovino je ena osrednjih slovenskih znanstvenih zgodovinopisnih revij, ki objavlja teme s področja novejše zgodovine (19. in 20. stoletje).
Revija izide trikrat letno v slovenskem jeziku in v naslednjih tujih jezikih: angleščina, nemščina, srbščina, hrvaščina, bosanščina, italijanščina, slovaščina in češčina. Članki izhajajo z izvlečki v angleščini in slovenščini ter povzetki v angleščini.
Študija se ukvarja z oblikovanjem napovednega modela,
namenjenega ugotavljanju, katere jezikovne in nejezikovne značilnosti
vplivajo na izbiro zaimkov v Shakespearovih igrah. V angleščini, ki se je
uporabljala v Shakespearovem obdobju, je razlikovanje med YOU in THOU, ki je
danes arhaično, še obstajalo. Običajno se navaja, da sta ga določala
relativni družbeni status ter osebna bližina govorca in naslovljenca. Vendar
pa je treba še ugotoviti, ali bo statistično strojno učenje potrdilo to
tradicionalno razlago. Proučuje se 23 značilnosti, izbranih z različnih
jezikoslovnih področij, kot so pragmatika, sociolingvistika in analiza
pogovora. Trije uporabljeni algoritmi – naivni Bayesov klasifikator,
odločitveno drevo in metoda podpornih vektorjev – so izbrani kot
ilustrativni nabor možnih modelov zaradi njihovih kontrastnih predpostavk in
učne pristranskosti. Opravita se dve napovedi, prva o binarnem (you/thou) razlikovanju in druga o
trinarnem (you/thou/thee) razlikovanju. Od vseh treh algoritmov daje
najboljše rezultate metoda podpornih vektorjev. Po ugotovitvah so
značilnosti, ki najbolje napovejo izbiro zaimka, besede iz neposrednega
jezikovnega konteksta. Izkazalo se je, da na napoved zaimka vpliva tudi več
drugih značilnosti, vključno z imenom govorca in naslovljenca, razliko v
statusu ter pozitivnim ali negativnim mnenjem.
Ključne besede: izrazi zaimkovnega naslavljanja, Shakespeare,
korpusno jezikoslovje, digitalna humanistika, statistično
modeliranje
This study creates a prediction model to identify which
linguistic and extra-linguistic features influence pronoun choices in the
plays of Shakespeare. In the English of Shakespeare’s time, the now-archaic
distinction between you and thou persisted, and is usually reported as being determined by
relative social status and personal closeness of speaker and addressee.
However, it remains to be determined whether statistical machine learning
will support this traditional explanation. 23 features are investigated,
having been selected from multiple linguistic areas, such as pragmatics,
sociolinguistics and conversation analysis. The three algorithms used, Naive
Bayes, decision tree and support vector machine, are selected as
illustrative of a range of possible models in light of their contrasting
assumptions and learning biases. Two predictions are performed, firstly on a
binary (you/thou) distinction and then on
a trinary (you/thou/thee) distinction. Of the three algorithms, the support
vector machine models score best. The features identified as the best
predictors of pronoun choice are the words in the direct linguistic context.
Several other features are also shown to influence the pronoun prediction,
including the names of the speaker and addressee, the status differential,
and positive and negative sentiment.
Keywords: pronominal address terms, Shakespeare, corpus
linguistics, digital humanities, statistical modelling
For several decades much research has been undertaken on the use of you, thou and thee in Shakespeare’s works. However, the results so far have yet to
arrive at an exact and conclusive answer regarding how these pronouns were
used.
This study combines the strengths of multiple research fields in an effort to
determine via hitherto unused methods which linguistic and extra-linguistic
features influence the choice of second person singular pronoun (you versus thou or thee) in the plays of William Shakespeare. Prior findings
in literary and linguistic studies are utilised to find which features could be
relevant in this choice, and tools and applications created for corpus
linguistics and computer science are exploited to analyse the data in a more
exact way than has so far been accomplished. Through these techniques, I hope to
identify which features can contribute to a more accurate prediction of pronoun
choice, in a model to mimic the pronoun use of Shakespeare.
It is worth observing at this point that it has not yet been determined whether
it is even possible to predict the pronoun based on linguistic features. Part of
the aim of this paper is to make a determination on this point. In other words,
is it possible to create a computational model that can predict which pronoun
will be used based on a set of linguistic and extra-linguistic features taken
from the text itself and selected on the basis of knowledge that we have of
English in the late 1500s and early 1600s? To accomplish this, all occurrences
of you, thou and thee are extracted from Shakespeare’s plays, and every instance is
manually coded for 23 linguistic and extra-linguistic features, creating data
which will serve to ascertain the answer to this primary question. A second
question to be addressed is whether some features perform better as predictors
of the pronoun choice than others. Thirdly, the issue of whether the use of
different algorithms affects the prediction outcomes will be considered.
Throughout this paper, italicised you, thou and thee refer to specific pronoun forms.
However, whereas you – in Early Modern English as in
contemporary English – does not exhibit any formal variation for pronoun case,
thou is strictly a nominative form with thee as its accusative/dative form. Thou and thee are therefore related
inflectional forms of a single pronoun lemma; you exists
in variation with both. Small capitals are used to indicate the pronoun lemmas,
thus: you and thou, where
thou includes both thou and
thee. Whenever discussing pronouns in this paper, I
am strictly referring to the singular second-person pronouns you, thou and thee that
are examined in this study.
Over the past few years, computational research has branched out into other research fields that are not necessarily closely connected to computer science. Digital Humanities (DH) is an umbrella term for all research that is computational but approaches the datasets investigated within, and/or addresses questions or problems that are of importance to, the disciplines of the humanities.
The popularity of Digital Humanities, a cross-domain field of study, is attributable to the fact that it does not diminish the differences between fields but rather operationalises this difference to solve difficulties that could not be dealt with within a single discipline. The role of computational methods in the humanities can be considered as that of a supporting character; in any DH computer modelling research, it should be kept in mind that the interpretation is as important at the suitability of a computational model and its outcomes.
In Early Modern English (EModE), two different second person singular
pronouns were used, namely the formally singular thou and the formally plural (but pragmatically also
respectful-singular) you, with only the latter
surviving the EModE period (Taavitsainen and Jucker 2003). The difference
between the uses of these two pronouns is evident from multiple literary
studies that have addressed Shakespeare’s work, work of his contemporaries,
and other documents from this era, such as Walker (2003)
and Busse (2002).
These studies suggest that unwritten social rules governed the use of these
pronouns, abiding by which rules was necessary in order to speak according
to society’s standards. The use of the two different pronouns acted as a
sign of relative status: you would be used to
superiors and thou towards inferiors. The choice
of pronoun can thus also operate as a subtle means of showing respect or
disrespect; using the pronouns in this way would have been natural and easy
to English native speakers of the period.
Shakespeare lived during the Early Modern English period, and thus used both
you and thou in his
writing. His work was written less than 100 years before thou and thee disappeared from the standard
language (surviving in dialects and archaicised registers, such as pious
addresses to the divinity). Thus we may straightforwardly posit that the
disappearance of thou was likely already in
progress around his time. Though obviously heightened in its use of
emotional and dramatic language and style to accommodate to the genre of the
play script, the language of Shakespeare – including the usage of the two
second-person pronouns – can be assumed to be a reasonably good
representation of the language used generally in social interaction and
conversation at that time (Calvo 1992).
Most studies of Shakespeare’s use of you and thou so far have been literary and nonnumeric
studies (Brown and Gilman 1960;
Quirk 1974; Calvo 1992); the relative few to
have used data-based or quantitative techniques did not implement any method
beyond directly comparing raw frequency counts (Busse 2003;
Mazzon 2003;
Stein 2003). Moreover, these studies did not look at all the extant
Shakespeare plays, but instead chose a few plays to focus on. Nonetheless,
these studies have demonstrated some patterns in the use of you and thou and thus
provide a workable foundation for a more in-depth study of the usage of
those two pronouns.
These prior studies support in the overall conclusion that the pronouns you and thou appear to
be used to support the explicit expression of respect, social status, and
familiarity. Quirk (1974) and Mazzon (2003) characterise the role of the
pronoun as a linguistic marker, whose usage can be seen as either marked or
unmarked. In other words, the use of a particular pronoun can be seen as
marked when it is used unexpectedly, for example when you is expected based on social status, but thou is used instead. Thus, in contrast to earlier studies
(Brown
and Gilman 1960), they do not perceive you and thou to be in direct contrast, and to have a more
variable interpretation than was assumed until then, based on the context it
occurs in. Calvo (1992) and Stein (2003) expand on this by concluding that
markedness of the pronoun is dependent on the context and the situation, in
addition to the pronoun choice depending on stable factors such as the
social statuses of, and the level of familiarity between, the characters in
Shakespeare’s plays; the speakers and addressees in this study – rather than
just the latter factors (Brown and Gilman 1960).
The emotive effect of the utterances within which the you/thou distinction is utilised is of importance as well;
feelings such as anger and love for another character may find expression
through pronoun choice. This is connected to the notion of respect, as, in
an angry remark, marked pronouns can be used to disrespect the addressee
based on their social status (Stein 2003).
As Stein (2003) and Busse (2006) already stressed in their studies, a study
of you and thou in
Shakespeare cannot and should not be limited to a single research
discipline. Rather, what is needed is a combination of literature,
sociolinguistics, pragmatics and conversation analysis, which are all useful
in capturing the complexity of pronominal address and the social
constrictions that may have underpinned the choice of one honorific
pronoun-form over the other.
As has already been mentioned, this is a strictly empirical study which attempts
to verify the findings of earlier research through a computational approach. The
use of a computational, statistical method is motivated by the goal of creating
a more objective representation of Shakespeare’s use of you and thou in his plays than has been
accomplished so far, since it does not require analysis of meaning-in-context by
a human being, but rather proceeds directly from quantitative measurements.
Three hypotheses were formulated on the basis of the literature:
This, being a null-hypothesis, is exactly what this study aims to falsify by
developing such a model. It is not likely that a single model will be able
to predict Shakespeare’s original choice of you or
thou based on linguistic and extra-linguistic
features, because this choice is dependent on so many factors. However, the
application of literature, sociolinguistics, pragmatics and conversation
analysis all combined into a computational model will be able to
successfully predict the pronoun choice as it includes all the factors that
might influence the choice for either you or thou.
A hierarchy will be established according to which the linguistic and extra-linguistic features are predicting the pronoun choice in the best performing model. It may be inferred from the literature that social status, age and sentiment are highly likely to be at the top of this hierarchy, among the most influential features; these three features have shown up most reliably in prior research.
The different learning biases and assumptions of the three algorithms applied in this study will reveal how the features interact with one another. The first algorithm, Naive Bayes, assumes all features are independent of one another, while the decision tree algorithm assumes that the features are all dependent on each other. Lastly, the support vector machine works with both dependent and independent features. I expect the set of features that will be included in the final model to be a combination of both dependent and independent features, and therefore the support vector machine algorithm to perform best. The three algorithms will be discussed in more detail later in the chapter Classification based on three algorithms.
The data for this study comes from the Encyclopaedia of
Shakespeare’s Language projectThe Two Noble Kinsmen and
Pericles: Prince of Tyre. A broadly annotated
version of the full Shakespeare corpus can be found onlineThe Arden Shakespeare.
The Encyclopaedia of Shakespeare’s Language corpus is richly annotated. However, some additional annotation was necessary to perform a full analysis of what extra-linguistic features could be predictors of the pronominal address term. The full set of features used in this study can be found in Table 1. The added features are briefly described here.
As a referent (such as a second person singular pronoun) is dependent on
context, the adjacent part of the utterance is used as a feature to test
the effect of co-text. Six co-textual words are included, i.e. a 7-gram
altogether. “LW” labels the words occurring on the left of the pronoun,
and “RW” the words on the right of the pronoun. Each of these words are
numbered based on their distance from the pronoun, e.g. LW3 is the third
word on the left of the pronoun. In corpus linguistics, collocations are
often examined within a three-word-window, meaning there are three words
on either side of the word of interest. While I am not necessarily
looking at specific collocations of you and
thou, the LW/RW features will look at
similarities and differences in co-textual words to see if they can
predict the pronoun choice.
Another feature noted as critical in prior studies is sentiment, that is
the use of the pronoun to convey positivity or negativity. Sentiment was
annotated with the use of the 7-gram described above. SentiStrength is a lexicon-based sentiment analysis program
that scores phrases with a score for positivity and negativity
(Thelwall
et al. 2010). Since SentiStrength was developed
to work with online comments rather than complete sentences as in formal
written English, it works well with n-grams too. The scores for
positivity and negativity are kept as separate variables.
The corpus already included metadata on the speakers; however, I wanted
to include age as well. The age of a character is often not given except
for when it is an important attribute of that character, making this
difficult to annotate. Therefore, Quennell and Johnson’s (2002)
character descriptions were used. The characters were sorted into a
trinary classification, with ‘adult’ as the default category. Any
deviations towards ‘younger’ or ‘older’ were based on textual references
or the character’s name, such as for ‘Old Man’ in King
Lear. Older characters were occasionally classified as such
based on the fact they had adult children with prominent roles in the
plays.
A more global feature is the location where the scene is set. This was difficult to annotate, due to the often unreliable stage directions. Instead of a nominal description for each scene location, I used a binary annotation of ‘public’ and ‘private’. The text itself was examined to determine the location based on what characters said about their location, but in addition Bate and Rasmussen’s (2007) annotation and Greenblatt, Cohen, Howard and Maus’ (1997) annotations were consulted. The use of these three resources enabled the binary manual annotation of location for every scene.
Besides the information about the speaker and the scene, information
regarding the addressee is essential when analysing character
interaction from a conversation analysis perspective. As a manual
annotation for addressee would be incredibly time consuming, I instead
used an automatic method which identifies the previous speaker as the
addressee of any given utterance. This is in line with the last-as-next
bias used in conversation analysis (Mazeland 2003). This means that,
even in larger group conversations, it is often expected that the last
speaker before the current speaker will also be the next speaker, thus
making it likely that the current speaker is addressing the last
speaker. If the utterances were interrupted by the start of a new scene
or other stage directions (e.g. someone walking into the scene), the
annotated addressee would be the next speaker
rather than the previous speaker for the first utterance after the
interruption.
Using the data for the social status of the speaker and the addressee, I also created a status differential. As the status category labels are numeric and ordered, this can be done by taking the difference between the two. For example, a king (status = 0) and a servant (status = 6) are distant in status, and thus will have a high status differential (here: 6). Between a king and a prince (status = 1), the difference is a lot smaller (here: 1). This absolute feature was automatically generated from the already annotated features.
A feature that had to be excluded is familiarity between characters (social distance). This data was not already available, and it was beyond the scope of this study to annotate this for all relevant character pairs. The literature has shown this to be a relevant feature. However, through the use of sentiment analysis, I have attempted to cover the complimentary and insulting aspects that could arise from high familiarity, and any lack thereof arising from low familiarity. Obviously, this does not cover all aspects of familiarity, but it means that this feature is not totally neglected.
Three different algorithms are used for the classification task, namely Naive
Bayes, decision trees and support vector machines. Whereas it would be ideal
to achieve a high precision and recall score, the main goal of this research
is to see whether it is even possible to predict the second person singular
pronoun choice through a computational application at
all. If this is indeed the case, what features contribute to this
prediction? It is thus more important to verify which features influence the
choice and to what extent they do so.
The reason for using three algorithms, and in particular these three, is their differences in learning biases and assumptions. Naive Bayes assumes all features are independent of one another, whereas decision tree attempts to create a dependent, hierarchical structure in the features. Support vector machine (SVM) is more complex and is able to combine both dependent and independent features. The addition of the latter algorithm will be particularly useful if the difference between the two simpler algorithm’s models is small.
As well as applying three algorithms, I will also look at the difference
between keeping thou and thee
separate and combining them into the one category thou. For this, I will run both a binary (you and thou) and a trinary (you, thou and thee) classification, to see whether this affects the
scores or changes which features are included in the best models.
I ran the three algorithms using the Waikato Environment for Knowledge
Analysis (Weka
The number of relevant instances of you/thou/thee
extracted from the dataset is 22,932, which makes up 99.5% of the total
number of such pronouns in the dataset. The pronouns were extracted using a
Python script with simple heuristics. About 0.5% was missed due to noise in
the dataset. The number of instances of you/thou/thee
that were extracted from each play range from 363 (in Macbeth) to 811 (in Coriolanus).
I attempted to improve or maintain the scores while making the model simpler
by excluding features, that is, through feature ablation. When there were
conflicting changes in the scores, the scores of precision and F-measure
were prioritised. I hoped to identify which features truly help predict the
pronoun by building the simplest but best performing model. The baseline
that the models were compared to is derived from the distribution of the
pronouns in the dataset, thus 62.6% of you and
37.4% thou.
I first took out groups of features that are related, rather than one feature at a time. Among the 23 features, I created six different groups. The first group related to the wider linguistic and social context (play, production date, genre, scene, location), while the second group was the closer linguistic co-text (n-gram). Information on the speaker (name, status, gender, age) and the addressee (name, status, gender, age, number of people) were groups 3 and 4. I kept status differential on its own, because it relates to multiple groups. Finally, the last group was sentiment (positive and negative). After the group ablation, I went back over the features to see if individual feature exclusions would improve the model further. This ensured the simplest and best model for each algorithm. The scores and the features included in each model are given in Tables 2, 3 and 4.
Table 2 shows the results of the trinary classification. As can be seen, each model performed significantly better than the baseline model, on all scores. The F-measure of the best model, the support vector machine model, is highlighted in bold.
Table 3 shows the results of the best models for the binary classification. The F-measure of the best model, again the support vector machine model, is highlighted in bold. This is also the best scoring model out of all models presented in this paper.
Overall, the final models contain similar sets of features. The exact compositions are given in Table 4. What is surprising is that the binary classification model for the decision tree is very different from the other models: it does not contain any of the words from the n-gram as a predictor, whereas the others did.
This study has given some new insights into the analysis of pronominal address
terms. Looking at the second person singular pronoun choice as a binary and a
trinary classification problem resulted in slightly different outcomes. Even
though the highest scores were achieved in the binary classification, one might
still wonder whether this is the best method for addressing the second person
singular pronoun choice. Looking back at prior studies on pronoun interpretation
and comparing them to the features used in this study, we can conclude that thee and thou are equal in their
opposition to you, with the main difference being their
grammatical role. From the model comparison, we have seen that the co-text is
most important when predicting the pronoun. This is evidence of the purely
grammatical difference between thou and thee and their overall similarity in other aspects. Therefore, both
linguistically and computationally, it makes more sense to perform a binary
classification.
Differences between the algorithms were observed, but all three algorithms easily outperformed the baseline. The support vector machine models performed best, but the scores for the Naive Bayes models were quite similar to those for the SVM models. A choice between these approaches could be based solely on the scores for accuracy, precision, recall and F-measure, or also by taking into account the complexity, which is significantly higher for the support vector machine models. The more nuanced models that the support vector machine creates, which include more features than the models of the other algorithms, may suggest that the extra complexity of SVM models is indeed beneficial.
The best predicting features were the LW and RW features, which supports the importance of the direct linguistic co-text. In particular RW1 appeared as the most important feature in predicting the second person singular pronominal address term. Other important features were the speaker’s name, addressee’s name, status differential, positive sentiment and negative sentiment, with additional support from the speaker’s gender, addressee’s status, addressee’s age, speaker’s age, and number of people addressed. Only six features were not included in any of the models: genre, play, production date, location, speaker’s status and addressee’s gender.
I am, therefore, now able to falsify the null-hypothesis that it is not possible to build a reliable prediction model based on linguistic and extra-linguistic features. All six models demonstrate that linguistic and extra-linguistic features substantially improve the prediction of the pronominal address term, as all six outperform the baseline.
The second hypothesis, about which features would be good predictors, was partially correct in predicting that social status, age and sentiment would be included in the best models. However, none of these features were the main predictor of pronoun choice; that was the immediate co-text.
With regard to the final hypothesis, it has been revealed that the features are indeed both dependent on and independent of each other. However, since the Naive Bayes models perform almost identically to the support vector machine models, we can say that the features are, for the most part, independent of one another.
The primary finding of this study is that it is indeed possible to build a
prediction model for the use of you versus thou with a singular referent in the plays of
Shakespeare that is based on linguistic and extra-linguistic features. Moreover,
in particular, the direct linguistic co-text of the second person singular
pronoun is important. Other important features include the speaker’s and
addressee’s names, status differential and both positive and negative sentiment.
All in all this suggests that the pronoun choice is influenced by several
linguistic and extra-linguistic features.
The best scoring algorithm and model was the support vector machine with 87.3% accuracy through its binary classification model.
For future research, I would recommend an exploration of other algorithms and
features that were left out of this study, such as morphology, word embeddings
and POS-tags. This will help us gain more information about the linguistic
co-text directly surrounding the second person singular pronoun, which will
likely give more insight into why this direct co-text is so important in
deciding the choice of you or thou. Moreover, including familiarity between characters (social
distance) as a feature would be beneficial, as this has been noted multiple
times in prior research as an influential factor, but was beyond the scope of
this study.
Although this study has not yet provided a comprehensive set of all the
linguistic and extra-linguistic features that influence the second person
singular pronoun choice in Shakespeare’s plays, it has definitely provided a
more objective and extensive analysis of the matter that furthers the research
into you and thou.
The research presented in this article was conducted in collaboration with the Encyclopaedia of Shakespeare’s Language project at Lancaster University. This project is funded by the UK’s Arts and Humanities Research Council (AHRC), grant reference AH/N002415/1. The Shakespeare corpus will be made publicly available in Summer 2019, first via the CQPweb interface and then through download at a later stage. Many thanks to Jonathan Culpeper and the rest of the team for their advice and support throughout the study.
Much research has been undertaken on the use of you, thou and thee in Shakespeare’s
works. However, the results so far have yet to arrive at an exact and conclusive
answer regarding how these pronouns were used. This study combines the strengths
of multiple research fields in an effort to determine via hitherto unused
computational methods which linguistic and extra-linguistic features influence
the second person singular pronoun choices in the plays of Shakespeare. In the
English of Shakespeare’s time, the now-archaic distinction between you and thou persisted, and
is usually reported as being determined by relative social status and personal
closeness of speaker and addressee. However, even between studies with similar
outcomes, the results vary massively on the degree of influence and by the
inclusion or exclusion of a wide range of other potential influencing factors.
Therefore, it remains to be determined whether statistical machine learning will
support this traditional explanation.
In this study, 23 linguistic and extra-linguistic features are investigated,
having been selected from multiple linguistic areas, such as pragmatics,
sociolinguistics and conversation analysis. The three algorithms used, Naive
Bayes, decision tree and support vector machine, are selected as illustrative of
a range of possible models in light of their contrasting assumptions and
learning biases. Two predictions are performed, firstly on a binary (you/thou) distinction and
then on a trinary (you/thou/thee) distinction, giving six final models to compare.
This is a strictly empirical study, which attempts to verify the findings of
earlier research through a computational approach. Its aim and main focus is to
try and find a pattern or model that best explains the use of second person
singular pronominal address terms in Shakespeare, rather than simply achieve the
best performing model.
The primary finding of this study is that it is indeed possible to build a
prediction model for the use of singular second person pronouns in the plays of
Shakespeare based on linguistic and extra-linguistic features. Moreover, in
particular, the direct linguistic context of the pronoun is the most important
feature in all of the models except one. Several other features are also
influencing the pronoun prediction, including the names of the speaker and
addressee, the status differential, and positive and negative sentiment.
Additionally, all three algorithms easily outperformed the baseline. Out of the
three algorithms, the support vector machine models score best. However, the
Naive Bayes models perform almost equally well. This reveals that the features
are, for the most part, independent of one another. When comparing the binary
and trinary classification outcomes, the binary models scored better than the
trinary ones. Looking back at prior studies on pronoun interpretation and
comparing them to the features used in this study, we can conclude that thee and thou are equal in their
opposition to you, with the main difference being their
grammatical role. Therefore, both linguistically and computationally, it makes
most sense to use the binary classification.
O uporabi zaimkov you, thou in thee v Shakespearovih delih je bilo opravljenih veliko
raziskav. Vendar rezultati doslej še niso dali natančnega in dokončnega odgovora
o tem, kako so se ti zaimki uporabljali. Študija združuje prednosti z različnih
raziskovalnih področij, da bi z računalniškimi metodami, ki doslej še niso bile
uporabljene, ugotovili, katere jezikovne in nejezikovne značilnosti vplivajo na
izbiro osebnega zaimka druge osebe ednine v Shakespearovih igrah. V angleščini,
ki se je uporabljala v Shakespearovem obdobju, je razlikovanje med YOU in THOU,
ki je danes arhaično, še obstajalo. Običajno se navaja, da sta ga določala
relativni družbeni status ter osebna bližina govorca in naslovljenca. Vendar pa
se tudi med študijami s podobnimi rezultati ti zelo razlikujejo glede stopnje
vplivanja ter upoštevanja ali neupoštevanja številnih drugih mogočih dejavnikov
vpliva. Zato je treba še ugotoviti, ali bo statistično strojno učenje potrdilo
to tradicionalno razlago.
V tej študiji se proučuje 23 jezikovnih in nejezikovnih značilnosti, izbranih z
različnih jezikoslovnih področij, kot so pragmatika, sociolingvistika in analiza
pogovora. Trije uporabljeni algoritmi – naivni Bayesov klasifikator, odločitveno
drevo in metoda podpornih vektorjev – so izbrani kot ilustrativni nabor možnih
modelov zaradi njihovih kontrastnih predpostavk in učne pristranskosti. Opravita
se dve napovedi, prva o binarnem ( you/thou) razlikovanju
in druga o trinarnem (you/thou/thee) razlikovanju, s
čimer dobimo šest končnih modelov, ki jih lahko primerjamo. Študija je strogo
empirična, njen cilj pa je z računalniškim pristopom preveriti ugotovitve
predhodnih raziskav. Osredotoča se predvsem na iskanje vzorca ali modela, ki bi
najbolje pojasnil uporabo izrazov zaimkovnega naslavljanja za drugo osebo ednine
pri Shakespearu, in ne le na oblikovanje modela, ki deluje najbolje.
Temeljna ugotovitev te študije je, da je resnično mogoče oblikovati napovedni
model za uporabo zaimkov za drugo osebo ednine v Shakespearovih igrah na podlagi
jezikovnih in nejezikovnih značilnosti. Poleg tega je neposredni jezikovni
kontekst zaimka najpomembnejša značilnost v vseh modelih razen v enem. Na
napoved zaimka vpliva tudi več drugih značilnosti, vključno z imenom govorca in
naslovljenca, razliko v statusu ter pozitivnim ali negativnim mnenjem. Vsi trije
algoritmi so tudi z lahkoto dosegli boljše rezultate od izhodišča. Od vseh treh
algoritmov daje najboljše rezultate metoda podpornih vektorjev. Vendar tudi
modeli naivnega Bayesovega klasifikatorja dosegajo skoraj enako dobre rezultate.
Iz tega izhaja, da so značilnosti večinoma neodvisne druga od druge. Primerjava
binarne in trinarne klasifikacije je pokazala, da so rezultati binarnih modelov
boljši od rezultatov trinarnih. Če primerjamo predhodne študije o interpretaciji
zaimkov z značilnostmi, uporabljenimi v tej študiji, lahko ugotovimo, da sta
zaimka thee in thou v opoziciji z
zaimkom you enakovredna, pri čemer je najpomembnejša
razlika njihova slovnična vloga. Zato je z jezikoslovnega in računalniškega
stališča najbolj smiselna uporaba binarne klasifikacije.