From Text to Bits: Making the Treaties of a Monk from the 16th Century "Understandable" to the Computer

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FROM TEXT TO BITS

MAKING THE PRINTED TREATIES OF A MONK FROM THE 16

^TH

CENTURY “UNDERSTANDABLE”

TO THE COMPUTER

Bastien DUBUISSON

Ph.D. Candidate in History

Greene’s Institute, Oxford, 19/10/19

@DubuissonBasti1

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“machine-readable”

■ ≠ Machine Translation

■ ≠ Natural Language Processing (NLP)

■ “Data in a data format that can be automatically [read and] processed by a computer, such as CSV, JSON, XML, etc. Machine-readable data must be structured data”

1. Create a digital corpus ex nihil 2. Text à Structured Data

3. Digital inquiries

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The Context

■ 1512: Tunica Christi

■ “Ostensiones reliquiarum”

■ “Heilthumsschriften”

Johannes ENEN, Medulla Gestorum Trevirensium, 1514, 2^nd edition in 1515

èTranslated into Latin by Johannes SCHECKMANN (⁺1531) Epitome alias medulla Gestorum Trevirorum,

Metz, 1517

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OPTICAL CHARACTER RECOGNITION

Creating Digital Copies of Texts

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Digital “Editions”

■ Raw text format è character strings (scripting languages)

■ Manually typing (txt, docx,…)

■ PROBLEM: large corpus of (longer) texts

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Optical Character Recognition (OCR)

■ Convert images of handwritten/printed text into machine-encoded text

■ Kraken (http://kraken.re)

■ Neural Network: “machine

learning system that simulates a large number of interconnected processing units that resemble abstract version of neurons”

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Optical Character Recognition (OCR)

■ Avoid a Silver Bullet Syndrome:

- High quality images (400dpi) è IIIF

- Pre-processing (deskewing, denoising) è GIMP

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Optical Character Recognition (OCR)

- Train the model

a. 1 model = 1. typeface b. length of a text

c. abbreviations

è TIME CONSUMING ACTIVITY

1. Humanistic Typeface (3/4 books)

2. Gothic Typeface (1/4 books)

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WHAT CAN BE

LOST

Adapting Texts for

Counting Words

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Tokenization

■ Slicing a text into tokens

■ Latin:

- appended conjunctions and adverbs (-que; -ne; -ve) e.g.: dominusque è dominus; que

- contraction of reflexive pronouns with idiomatic reinforcements e.g.: seipsum è se; ipsum

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Lemmatization

■ Latin = highly inflected language

■ Lemma = dictionnary form of a word

e.g.: amabit; amaverunt; amabantur è amo

■ TreeTagger

è Unrecognized words - Neolatin forms - German words

- Inconsistent spelling è Remaining unknown lemmas

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WHAT CAN BE FOUND

Discoveries by Means of Digital Inquiries

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Text Comparisons

■ Scheckmann’s Heilthumsschriften à chronicles

■ Use of hagiographic texts?

è Identify common passages (even ≠ verbatim) è textreuse package (“Optimal alignments”)

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Text Comparisons

■ Passages on relics and miracles (inventio, translatio, miraculum,…)

■ Altar dedications (St Maximin & St Paulin)

■ Gesta Treverorum

■ Flores Epytaphii Sanctorum (Thiofrid of Echternach, 12^th cent.)

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Stylometry (Authorship Attribution)

■ Stylometry: “the quantitative study of literary style through computational distant reading methods (...), [that is] based on the observation that authors tend to write in relatively consistent, recognizable and unique ways”

– Laramée, D., Introduction to Stylometry with Python

■ Disputed authorship of:

- Historia Excidii Sancti Maximini - Supplicatio ad Caesarem

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Stylometry

■ “Stop words” (or “function words”) à used unconsciously

à topic independent

■ “Impostors” (Nicholas of Cusa & Erasmus of Rotterdam)

■ 200 MFW è 65 MFFW

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Stylometry

PC1

PC2 x

y

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Concluding note

bastien.dubuisson@uni.lu

@DubuissonBasti1

■ Missing ‘traditional’ study

■ Creation of new digital corpora

■ Thoughtful use of digital tools

■ Challenge for historical languages

■ New research opportunities