Notebook

Proper nouns with or without definite articles (Nestle1904LFT)¶

Table of content ¶

1 - Introduction
- 1.1 - Why is this relevant?
- 1.2 - Translating into Text-Fabric queries
2 - Load Text-Fabric app and data
3 - Performing the queries
- 3.1 - TBD
- 3.2 - TBD
4 - Discussion
- 4.1 - Textual variants
5 - Attribution and footnotes
6 - Required libraries

1 - Introduction ¶

Back to TOC ¶

In this Jupyter NoteBook we will examine the use of proper nouns with or without definite articles in the Greek New Testament.

Proper nouns in Biblical Greek are typically used without a definite article when they function as names. For example: Πέτρος εἶπε... (Peter said...)

1.1 - Why is this relevant? ¶

In the Greek New Testament, the presence or absence of definite articles with proper nouns can carry specific grammatical and semantic implications. The pressence or absence of the article can make a difference for a few reasons, e.g.:

Identifiability: The presence of a definite article (e.g., ὁ, ἡ, τὸ) before a proper noun indicates that the noun refers to a specific, identifiable entity. It often implies that the noun is known to both the speaker and the audience. On the other hand, the absence of the definite article suggests a more general or less specific reference.
Emphasis: The use or omission of the definite article can be employed for emphasis or to highlight certain aspects of the proper noun. When the definite article is present, it draws attention to the individual or highlight particular qualities or characteristics. Without the definite article, the focus may shift more to the general category or class to which the noun belongs.
Unique versus Common Names: Proper nouns in Greek can be classified as either unique or common names. Unique names refer to specific individuals or places, such as Ἰησοῦς or Ἱεροσόλυμα. These often appear with the definite article when referring to a well-known individual or location. Common names, on the other hand, are more general and do not require the definite article. For example, ἄνθρωπος can refer to any man in general.
Anaphoric References: In Greek, the presence or absence of the definite article can indicate anaphoric references, referring back to a previous mention. The use of the definite article can signal that the proper noun is referring to someone or something previously introduced or discussed, providing continuity and clarity in the discourse.

It would be of interest if the Text-Fabric dataset could provide clues to how an article is uses.

1.2 - Translating into Text-Fabric queries ¶

Back to TOC ¶

The wg (wordgroup) nodes in the Low Fat Tree Text-Fabric data contains a feature called wgrule which can take the value DetNP.

2 - Load Text-Fabric app and data ¶

Back to TOC ¶

In [1]:

%load_ext autoreload
%autoreload 2

In [2]:

# Loading the Text-Fabric code
# Note: it is assumed Text-Fabric is installed in your environment
from tf.fabric import Fabric
from tf.app import use

In [3]:

# load the N1904 app and data
N1904 = use ("tonyjurg/Nestle1904LFT", version="0.6", hoist=globals())

Locating corpus resources ...

The requested app is not available offline
	~/text-fabric-data/github/tonyjurg/Nestle1904LFT/app not found

Status: latest release online v0.6 versus None locally

downloading app, main data and requested additions ...

app: ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/app

The requested data is not available offline
	~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 not found

Status: latest release online v0.6 versus None locally

downloading app, main data and requested additions ...

data: ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6

   |     0.22s T otype                from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     2.28s T oslots               from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.56s T book                 from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.59s T normalized           from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.50s T after                from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.57s T wordtranslit         from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.59s T wordunacc            from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.47s T verse                from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.60s T unicode              from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.60s T word                 from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.49s T chapter              from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |      |     0.06s C __levels__           from otype, oslots, otext
   |      |     1.85s C __order__            from otype, oslots, __levels__
   |      |     0.07s C __rank__             from otype, __order__
   |      |     3.40s C __levUp__            from otype, oslots, __rank__
   |      |     1.91s C __levDown__          from otype, __levUp__, __rank__
   |      |     0.22s C __characters__       from otext
   |      |     0.96s C __boundary__         from otype, oslots, __rank__
   |      |     0.04s C __sections__         from otype, oslots, otext, __levUp__, __levels__, book, chapter, verse
   |      |     0.22s C __structure__        from otype, oslots, otext, __rank__, __levUp__, book, chapter, verse
   |     0.43s T booknumber           from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.50s T bookshort            from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.49s T case                 from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.32s T clausetype           from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.55s T containedclause      from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.41s T degree               from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.57s T gloss                from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.47s T gn                   from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.04s T headverse            from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.32s T junction             from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.55s T lemma                from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.51s T lex_dom              from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.52s T ln                   from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.42s T markafter            from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.40s T markbefore           from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.40s T markorder            from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.44s T monad                from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.44s T mood                 from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.52s T morph                from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.55s T nodeID               from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.49s T nu                   from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.49s T number               from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.44s T person               from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.44s T punctuation          from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.66s T ref                  from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.65s T reference            from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.50s T roleclausedistance   from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.46s T sentence             from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.50s T sp                   from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.50s T sp_full              from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.54s T strongs              from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.45s T subj_ref             from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.43s T tense                from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.45s T type                 from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.44s T voice                from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.38s T wgclass              from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.33s T wglevel              from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.37s T wgnum                from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.34s T wgrole               from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.37s T wgrolelong           from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.40s T wgrule               from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.33s T wgtype               from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.49s T wordlevel            from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.49s T wordrole             from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
   |     0.50s T wordrolelong         from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6

TF: TF API 12.1.5, tonyjurg/Nestle1904LFT/app v3, Search Reference
Data: tonyjurg - Nestle1904LFT 0.6, Character table, Feature docs

Node types

Name	# of nodes	# slots / node	% coverage
book	27	5102.93	100
chapter	260	529.92	100
verse	7943	17.35	100
sentence	8011	17.20	100
wg	105430	6.85	524
word	137779	1.00	100

Sets: no custom sets
Features:

Nestle 1904 (Low Fat Tree)

after

str

✅ Characters (eg. punctuations) following the word

book

str

✅ Book name (in English language)

booknumber

int

✅ NT book number (Matthew=1, Mark=2, ..., Revelation=27)

bookshort

str

✅ Book name (abbreviated)

case

str

✅ Gramatical case (Nominative, Genitive, Dative, Accusative, Vocative)

chapter

int

✅ Chapter number inside book

clausetype

str

✅ Clause type details (e.g. Verbless, Minor)

containedclause

str

🆗 Contained clause (WG number)

degree

str

✅ Degree (e.g. Comparitative, Superlative)

gloss

str

✅ English gloss

str

✅ Gramatical gender (Masculine, Feminine, Neuter)

headverse

str

✅ Start verse number of a sentence

junction

str

✅ Junction data related to a wordgroup

lemma

str

✅ Lexeme (lemma)

lex_dom

str

✅ Lexical domain according to Semantic Dictionary of Biblical Greek, SDBG (not present everywhere?)

str

✅ Lauw-Nida lexical classification (not present everywhere?)

markafter

str

🆗 Text critical marker after word

markbefore

str

🆗 Text critical marker before word

markorder

str

Order of punctuation and text critical marker

monad

int

✅ Monad (smallest token matching word order in the corpus)

mood

str

✅ Gramatical mood of the verb (passive, etc)

morph

str

✅ Morphological tag (Sandborg-Petersen morphology)

nodeID

str

✅ Node ID (as in the XML source data)

normalized

str

✅ Surface word with accents normalized and trailing punctuations removed

str

✅ Gramatical number (Singular, Plural)

number

str

✅ Gramatical number of the verb (e.g. singular, plural)

otype

str

person

str

✅ Gramatical person of the verb (first, second, third)

punctuation

str

✅ Punctuation after word

ref

str

✅ Value of the ref ID (taken from XML sourcedata)

reference

str

✅ Reference (to nodeID in XML source data, not yet post-processes)

roleclausedistance

str

⚠️ Distance to the wordgroup defining the syntactical role of this word

sentence

int

✅ Sentence number (counted per chapter)

str

✅ Part of Speech (abbreviated)

sp_full

str

✅ Part of Speech (long description)

strongs

str

✅ Strongs number

subj_ref

str

🆗 Subject reference (to nodeID in XML source data, not yet post-processes)

tense

str

✅ Gramatical tense of the verb (e.g. Present, Aorist)

type

str

✅ Gramatical type of noun or pronoun (e.g. Common, Personal)

unicode

str

✅ Word as it apears in the text in Unicode (incl. punctuations)

verse

int

✅ Verse number inside chapter

voice

str

✅ Gramatical voice of the verb (e.g. active,passive)

wgclass

str

✅ Class of the wordgroup (e.g. cl, np, vp)

wglevel

int

🆗 Number of the parent wordgroups for a wordgroup

wgnum

int

✅ Wordgroup number (counted per book)

wgrole

str

✅ Syntactical role of the wordgroup (abbreviated)

wgrolelong

str

✅ Syntactical role of the wordgroup (full)

wgrule

str

✅ Wordgroup rule information (e.g. Np-Appos, ClCl2, PrepNp)

wgtype

str

✅ Wordgroup type details (e.g. group, apposition)

word

str

✅ Word as it appears in the text (excl. punctuations)

wordlevel

str

🆗 Number of the parent wordgroups for a word

wordrole

str

✅ Syntactical role of the word (abbreviated)

wordrolelong

str

✅ Syntactical role of the word (full)

wordtranslit

str

🆗 Transliteration of the text (in latin letters, excl. punctuations)

wordunacc

str

✅ Word without accents (excl. punctuations)

oslots

none

Settings:

specified

apiVersion: 3
appName: tonyjurg/Nestle1904LFT
appPath:
C:/Users/tonyj/text-fabric-data/github/tonyjurg/Nestle1904LFT/app
commit: no value
css: ''
dataDisplay:
- excludedFeatures:
  - orig_order
  - verse
  - book
  - chapter
- noneValues:
  - none
  - unknown
  - no value
  - NA
  - ''
- showVerseInTuple: 0
- textFormat: text-orig-full
docs:
- docBase: https://github.com/tonyjurg/Nestle1904LFT/blob/main/docs/
- docPage: about
- docRoot: https://github.com/tonyjurg/Nestle1904LFT
- featureBase:
  https://github.com/tonyjurg/Nestle1904LFT/blob/main/docs/features/<feature>.md
interfaceDefaults: {fmt: layout-orig-full}
isCompatible: True
local: no value
localDir:
C:/Users/tonyj/text-fabric-data/github/tonyjurg/Nestle1904LFT/_temp
provenanceSpec:
- corpus: Nestle 1904 (Low Fat Tree)
- doi: notyet
- org: tonyjurg
- relative: /tf
- repo: Nestle1904LFT
- repro: Nestle1904LFT
- version: 0.6
- webBase: https://learner.bible/text/show_text/nestle1904/
- webHint: Show this on the Bible Online Learner website
- webLang: en
- webUrl:
  https://learner.bible/text/show_text/nestle1904/<1>/<2>/<3>
- webUrlLex: {webBase}/word?version={version}&id=<lid>
release: no value
typeDisplay:
- book:
  - condense: True
  - hidden: True
  - label: {book}
  - style: ''
- chapter:
  - condense: True
  - hidden: True
  - label: {chapter}
  - style: ''
- sentence:
  - hidden: 0
  - label: #{sentence} (start: {book} {chapter}:{headverse})
  - style: ''
- verse:
  - condense: True
  - excludedFeatures: chapter verse
  - label: {book} {chapter}:{verse}
  - style: ''
- wg:
  - hidden: 0
  - label:
    #{wgnum}: {wgtype} {wgclass} {clausetype} {wgrole} {wgrule} {junction}
  - style: ''
- word:
  - base: True
  - features: lemma
  - featuresBare: gloss
  - surpress: chapter verse
writing: grc

TF API: names N F E L T S C TF Fs Fall Es Eall Cs Call directly usable

In [4]:

# The following will push the Text-Fabric stylesheet to this notebook (to facilitate proper display with notebook viewer)
N1904.dh(N1904.getCss())

In [5]:

# Set default view in a way to limit noise as much as possible.
N1904.displaySetup(condensed=True, multiFeatures=False,queryFeatures=False)

3 - Performing the queries ¶

Back to TOC ¶

3.1 - Determine articles in combination with proper nouns ¶

Back to TOC ¶

This query selects each phrase containing (at least) two words: the article (a) and the proper noun (b).

The query without congruence conditions yealds 9500 results. Adding congruence conditions limits the results step by step: adding case limits it to 8521, adding num to 8320, and adding gn further to 8067.

In [6]:

# Define the query template
ArticulatedQuery = '''
wg wgrule=DetNP
  a:word sp=noun
  b:word sp=det
'''

# The following will create a list containing ordered tuples consisting of node numbers of the items as they appear in the query
ArticulatedResult = N1904.search(ArticulatedQuery)

  0.35s 25918 results

The list ArticulatedResult contains ordered tuples with the node numbers of the found phrase, det, and noun combinations. The data can now further be analyzed. The following table provides insight in which phrase types most frequently contain an articulated proper noun. This table still needs to deal with multiple findings in the same phrase.

In [7]:

# Library to format table
from tabulate import tabulate

ResultDict = {}
for (wg,det,noun) in ArticulatedResult:
    PhraseFunction=F.wgrole.v(wg)
    # Check if this Wordgroup Role already exists in ResultDict
    if PhraseFunction in ResultDict:
         # If it exists, add the count to the existing value
         ResultDict[PhraseFunction]+=1
    else:
         # If it doesn't exist, initialize the count as the value
         ResultDict[PhraseFunction]=1
    
# Convert the dictionary into a list of key-value pairs and sort it according to frequency
UnsortedTableData = [[key, value] for key, value in ResultDict.items()]
TableData= sorted(UnsortedTableData, key=lambda row: row[1], reverse=True)

# Produce the table
headers = ["Word Group function","Frequency"]
print(tabulate(TableData, headers=headers, tablefmt='fancy_grid'))

╒═══════════════════════╤═════════════╕
│ Word Group function   │   Frequency │
╞═══════════════════════╪═════════════╡
│                       │       14612 │
├───────────────────────┼─────────────┤
│ s                     │        4992 │
├───────────────────────┼─────────────┤
│ o                     │        4475 │
├───────────────────────┼─────────────┤
│ adv                   │         720 │
├───────────────────────┼─────────────┤
│ io                    │         549 │
├───────────────────────┼─────────────┤
│ p                     │         423 │
├───────────────────────┼─────────────┤
│ aux                   │         105 │
├───────────────────────┼─────────────┤
│ o2                    │          42 │
╘═══════════════════════╧═════════════╛

There is a dificulty to determine how many different phrases are involved..... The verselocation below points to the start of the phrase (clearest indicated by the high number for Luke 3:23 which starts a long geneology).

In [8]:

PhrasesDict = {}
for (wg,det,noun) in ArticulatedResult:
    location=T.sectionFromNode(wg)
    # Check if this location already exists in PhrasesDict
    if location in PhrasesDict:
         # If it exists, it is a duplicate: add the count to the existing value
         PhrasesDict[location]+=1
    else:
         # If it doesn't exist, mark it down
         PhrasesDict[location]=1
    
# Convert the dictionary into a list of key-value pairs and sort it according to frequency
UnsortedTableData = [[key, value] for key, value in PhrasesDict.items()]
TableData= sorted(UnsortedTableData, key=lambda row: row[1], reverse=True)

# In this example the table will be truncated 
max_rows = 10  # Set your desired number of rows here
TruncatedTable = TableData[:max_rows]

# Produce the table
headers = ["verselocation","Frequency"]
print(tabulate(TruncatedTable, headers=headers, tablefmt='fancy_grid'))

# Add a warning using markdown (API call A.dm) allowing it to be printed in bold type
N1904.dm("**Warning: table truncated!**")

╒═══════════════════════════╤═════════════╕
│ verselocation             │   Frequency │
╞═══════════════════════════╪═════════════╡
│ ('Ephesians', 1, 7)       │         323 │
├───────────────────────────┼─────────────┤
│ ('Colossians', 1, 13)     │         205 │
├───────────────────────────┼─────────────┤
│ ('Ephesians', 3, 4)       │         131 │
├───────────────────────────┼─────────────┤
│ ('Acts', 3, 25)           │         123 │
├───────────────────────────┼─────────────┤
│ ('II_Corinthians', 8, 18) │          96 │
├───────────────────────────┼─────────────┤
│ ('Revelation', 19, 15)    │          90 │
├───────────────────────────┼─────────────┤
│ ('Revelation', 11, 18)    │          79 │
├───────────────────────────┼─────────────┤
│ ('II_Corinthians', 4, 4)  │          76 │
├───────────────────────────┼─────────────┤
│ ('Ephesians', 1, 9)       │          68 │
├───────────────────────────┼─────────────┤
│ ('Revelation', 3, 12)     │          62 │
╘═══════════════════════════╧═════════════╛

Warning: table truncated!

The following will show the first result in a graph:

In [10]:

# Note the options "condensed=True, multiFeatures=False,queryFeatures=False" are included below due to the earlier N1904.displaySetup(...)
N1904.show(ArticulatedResult, start=1, end=1)

verse 1

Matthew 1:2

verse Matthew 1:2

sentence #2 (start: Matthew 1:2)

wg #11: cl* Conj13CL

wg #12: cl S-V-O

Ἀβραὰμ

ἐγέννησεν

wg #13: np o DetNP

τὸν

Ἰσαάκ,

wg #14: group

wg #15: cl S-V-O

Ἰσαὰκ

δὲ

wg #15: cl S-V-O

ἐγέννησεν

wg #16: np o DetNP

τὸν

Ἰακώβ,

wg #17: group

wg #18: cl S-V-O

Ἰακὼβ

δὲ

wg #18: cl S-V-O

ἐγέννησεν

wg #19: o NpaNp

wg #20: np DetNP

τὸν

Ἰούδαν

wg #21: group

καὶ

wg #22: np DetNP

τοὺς

wg #23: np NPofNP

ἀδελφοὺς

αὐτοῦ,

4 - Discussion ¶

Back to TOC ¶

4.1 - Textual variants ¶

Back to TOC ¶

The research in articulated proper nouns is also relevant in relation to variant readings. Consider for exemple Acts 18:1.

NA28:

Μετὰ ταῦτα χωρισθεὶς ἐκ τῶν Ἀθηνῶν ἦλθεν εἰς Κόρινθον

Stephanus Textus Receptus 1550:

Μετὰ δὲ ταῦτα χωρισθεὶς ὁ Παῦλος ἐκ τῶν Ἀθηνῶν ἦλθεν εἰς Κόρινθον

5 - Attribution and footnotes ¶

Back to TOC ¶

Atrribution:¶

Thanks to Prof. Willem van Peursen (VU) for pointing me to this question and raising the following interesting issue in respect to the use of a definite article:

In Muraoka’s Why Read the Bible in the Original Languages,¹ I found the following examples:

(...) The definite article e.g. “the truth will set you free” (John 8:32; TJ) how to translate in Japanese, which doesn’t have the definite article.

Footnotes:¶

¹ Muraoka, Takamitsu. Why Read the Bible in the Original Languages? (Leuven: Peeters Publishers, 2020), 71.

6 - Required libraries ¶

Back to TOC ¶

The scripts in this notebook require (beside text-fabric) the following Python libraries to be installed in the environment:

{none}

You can install any missing library from within Jupyter Notebook using eitherpip or pip3.

In [ ]:

Proper nouns with or without definite articles (Nestle1904LFT)¶

Table of content ¶

1 - Introduction ¶

1.1 - Why is this relevant? ¶

1.2 - Translating into Text-Fabric queries ¶

2 - Load Text-Fabric app and data ¶

3 - Performing the queries ¶

3.1 - Determine articles in combination with proper nouns¶

4 - Discussion¶

4.1 - Textual variants ¶

5 - Attribution and footnotes¶

Atrribution:¶

Footnotes:¶

6 - Required libraries¶

3.1 - Determine articles in combination with proper nouns ¶

4 - Discussion ¶

5 - Attribution and footnotes ¶

6 - Required libraries ¶