Work in progress!
Consider the following example from Luke 7:38:
(...) καὶ στᾶσα ὀπίσω παρὰ τοὺς πόδας αὐτοῦ κλαίουσα τοῖς δάκρυσιν ἤρξατο βρέχειν τοὺς πόδας αὐτοῦ καὶ ταῖς θριξὶν τῆς κεφαλῆς αὐτῆς ἐξέμασσεν καὶ κατεφίλει τοὺς πόδας αὐτοῦ καὶ ἤλειφεν τῷ μύρῳ.
Here the word πόδας (feet) is used 3 times in the same sentence (which started in verse 36).
The question is if the repetion of πόδας is of exegetical importance, given the fact that the second and third occurence could be refered to using the pronoun them.
The idea is to analyse the number of occurences of identical nouns per sentence and score them.
%load_ext autoreload
%autoreload 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
# 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
The requested data is not available offline ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 not found
| 0.22s T otype from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 2.33s T oslots from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.58s T book from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.61s T word from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.60s T wordtranslit from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.60s T wordunacc from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.60s T normalized from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.49s T after from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.46s T verse from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.61s T unicode from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.50s T chapter from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | | 0.06s C __levels__ from otype, oslots, otext | | 1.80s C __order__ from otype, oslots, __levels__ | | 0.08s C __rank__ from otype, __order__ | | 3.39s C __levUp__ from otype, oslots, __rank__ | | 1.97s C __levDown__ from otype, __levUp__, __rank__ | | 0.23s 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 | | 0.23s C __structure__ from otype, oslots, otext, __rank__, __levUp__, book, chapter, verse | 0.46s T booknumber from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.52s T bookshort from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.47s T case from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.38s T clausetype from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.56s T containedclause from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.42s T degree from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.59s T gloss from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.49s T gn from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.03s 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 | 0.58s T lemma from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.52s T lex_dom from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.58s T ln from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.44s T markafter from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.41s T markbefore from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.43s T markorder from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.46s 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 | 0.53s 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 | 0.53s 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 | 0.46s T punctuation from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.76s 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 | 0.49s T roleclausedistance from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.48s T sentence from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.51s T sp from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.51s 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 | 0.46s T subj_ref from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.45s T tense from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.46s T type from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.45s 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 | 0.35s T wgnum from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.35s T wgrole from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.36s T wgrolelong from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.39s T wgrule from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.34s 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 | 0.51s T wordrole from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6 | 0.52s T wordrolelong from ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6
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 |
3
tonyjurg/Nestle1904LFT
C:/Users/tonyj/text-fabric-data/github/tonyjurg/Nestle1904LFT/app
''
orig_order
verse
book
chapter
none
unknown
NA
''
0
text-orig-full
https://github.com/tonyjurg/Nestle1904LFT/blob/main/docs/
about
https://github.com/tonyjurg/Nestle1904LFT
https://github.com/tonyjurg/Nestle1904LFT/blob/main/docs/features/<feature>.md
layout-orig-full
}True
C:/Users/tonyj/text-fabric-data/github/tonyjurg/Nestle1904LFT/_temp
Nestle 1904 (Low Fat Tree)
notyet
tonyjurg
/tf
Nestle1904LFT
Nestle1904LFT
0.6
https://learner.bible/text/show_text/nestle1904/
Show this on the Bible Online Learner website
en
https://learner.bible/text/show_text/nestle1904/<1>/<2>/<3>
{webBase}/word?version={version}&id=<lid>
True
True
{book}
''
True
True
{chapter}
''
0
#{sentence} (start: {book} {chapter}:{headverse})
''
True
chapter verse
{book} {chapter}:{verse}
''
0
#{wgnum}: {wgtype} {wgclass} {clausetype} {wgrole} {wgrule} {junction}
''
True
lemma
gloss
chapter verse
grc
# The following will push the Text-Fabric stylesheet to this notebook (to facilitate proper display with notebook viewer)
N1904.dh(N1904.getCss())
# Set default view in a way to limit noise as much as possible.
N1904.displaySetup(condensed=True, multiFeatures=False,queryFeatures=False)
This code will produce .....
TBA
Thanks to Prof. Willem van Peursen (VU) for pointing me to this interesting issue by mentioning:
In Muraoka’s Why Read the Bible in the Original Languages,1 I found the following examples:
(...) Repetition (e.g. why are “feet” repeated three times in Luke 7:38) (...)
1 Muraoka, Takamitsu. Why Read the Bible in the Original Languages? (Leuven: Peeters Publishers, 2020), 73.
The scripts in this notebook require (beside text-fabric
) the following Python libraries to be installed in the environment:
???
You can install any missing library from within Jupyter Notebook using eitherpip
or pip3
.