TY - JOUR

T1 - You, Thou and Thee

T2 - A Statistical Analysis of Shakespeare's Use of Pronominal Address Terms

AU - van Dorst, I.

PY - 2019/6/11

Y1 - 2019/6/11

N2 - 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. © 2019 Institute of Contemporary History. All rights reserved.

AB - 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. © 2019 Institute of Contemporary History. All rights reserved.

KW - Corpus linguistics

KW - Digital humanities

KW - Pronominal address terms

KW - Shakespeare

KW - Statistical modelling

M3 - Journal article

VL - 59

SP - 29

EP - 45

JO - Prispevki za Novejso Zgodovino

JF - Prispevki za Novejso Zgodovino

IS - 1

ER -