A hapax legomenon (plural: hapax legomena) is a term used in linguistics and literary analysis to refer to a word or idiomatic expression that appears only once in a specific corpus. In the context of the Bible, hapax legomena are words that occur only once in the entire biblical text, posing unique challenges for translators and scholars. Since these words lack additional occurrences for context, their meanings can often remain obscure or debated.
The same may be true for some dis legomena, words that appear only twice within a corpus. While technically not a hapax legomenon, the Greek word ἐπιούσιος, which occurs only twice in the Lord’s Prayer (Matthew 6:11 and Luke 11:3), exemplifies a similar issue. Its precise meaning is uncertain, with interpretations ranging from "daily" to "necessary" to "supernatural." 1
Not all New Testament hapax legomena, however, are obscure or lack comparison material. For instance:
Names of people, places, or objects are often clear; for example, the name Δανιήλ (Daniel) has an unambiguous meaning.
Hapax legomena with Septuagint parallels: Certain New Testament hapax legomena can be cross-referenced with the Septuagint (LXX), which can provide additional insights.Take for example the use of the Greek verb ἱερουργέω ('to minister as a priest') in Romans 15:16. Zondervan Illustrated Bible Backgrounds Commentary provides the following comment:
“The priestly duty of proclaiming the gospel of God” renders a difficult Greek phrase, with “gospel” as the object of the verb “offer sacrifice.” But a parallel is found in 4 Maccabees 7:8, which speaks of “administrators of the law” (lit., “those who serve the law as priests”).2
While a list of hapax legomena can be computed with relative ease, its usefulness may be limited if presented without contextual or semantic information. In this notebook, we will categorize the New Testament hapax legomena by their semantic domains, as defined by Louw-Nida,4 to enhance interpretative value. This semantic data is available in feature ln.
%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 app and data
N1904 = use ("CenterBLC/N1904", version="1.0.0", hoist=globals())
Locating corpus resources ...
| Name | # of nodes | # slots / node | % coverage |
|---|---|---|---|
| book | 27 | 5102.93 | 100 |
| chapter | 260 | 529.92 | 100 |
| verse | 7944 | 17.34 | 100 |
| sentence | 8011 | 17.20 | 100 |
| group | 8945 | 7.01 | 46 |
| clause | 42506 | 8.36 | 258 |
| wg | 106868 | 6.88 | 533 |
| phrase | 69007 | 1.90 | 95 |
| subphrase | 116178 | 1.60 | 135 |
| word | 137779 | 1.00 | 100 |
3CenterBLC/N1904C:/Users/tonyj/text-fabric-data/github/CenterBLC/N1904/appgdb630837ae89b9468c9e50d13bda05cfd3de4f18''[]noneunknownNA :text-orig-fullhttps://github.com/CenterBLC/N1904/tree/main/docsabouthttps://github.com/CenterBLC/N1904https://github.com/CenterBLC/N1904/blob/main/docs/features/<feature>.mdREADMEtext-orig-full}TruelocalC:/Users/tonyj/text-fabric-data/github/CenterBLC/N1904/_tempmainNestle 1904 Greek New Testament10.5281/zenodo.13117910[]CenterBLC/tfN1904N19041.0.0https://learner.bible/text/show_text/nestle1904/Show this on the websiteenhttps://learner.bible/text/show_text/nestle1904/<1>/<2>/<3>{webBase}/word?version={version}&id=<lid>1.0.0True{typ} {function} {rela} \\ {cls} {role} {junction}''{typ} {function} {rela} \\ {typems} {role} {rule}''True{typ} {function} {rela} \\ {typems} {role} {rule}''{typ} {function} {rela} \\ {role} {rule}''{typ} {function} {rela} \\ {typems} {role} {rule}''True{book} {chapter}:{verse}''True{typems} {role} {rule} {junction}''lemmaspgloss]grcDisplay is setup for viewtype syntax-view
See here for more information on viewtypes
# The following will push the Text-Fabric stylesheet to this notebook (to facilitate proper display with notebook viewer)
N1904.dh(N1904.getCss())
The underlying principle of the script below is rather straightforward. However, the primary challenge lies in determining the feature to be employed in the identification of hapax legomena. The two most obvious options are:
normalized: This is basicly a 'cleaned up' version of the surface text. It does take into account forms where inflections of verbs and declensions of nouns are considered as separate words. The normalization is required to account for variations in accentuation.
lemma: here the base or root form of words, known as lemmas, serves as the basis for frequency calculations. When based upon feature "lemma", there are a few instances reported which refer to a specific sense associated with that lemma. For example, lemma="βάτος (II)" is only found once (in Luke 16:6), while lemma="βάτος (I)" is found five times.
In the N1904-TF dataset, we face an additional challenge due to its data design: the lemma feature is associated not only with word nodes but also with phrase and subphrase nodes. Therefore, the following approach does not work with this dataset:
FeatureFrequenceLists=Fs("lemma").freqList()
foundLemmata=0
for lemma, freq in FeatureFrequenceLists:
if freq==1:
print (lemma)
foundLemmata+=1
print (f'Found {foundLemmata} lemmata')
Found 0 lemmata
To build an effective script, we will iterate over all word nodes, retrieve each lemma, and create a dictionary to store this information. For each new lemma encountered, the dictionary will record the lemma itself, initialize a counter at 1, and store the node’s numeric value for its first occurrence (or, in the case of a hapax legomenon, its only instance). For subsequent occurrences of an already recorded lemma, the counter will increment accordingly.
# Initialize an empty dictionary to store lemma counts and first occurrences
lemmaInfoDict = {}
# Iterate over each word in the Greek New Testament
for word in F.otype.s("word"): # assuming F.otype.s("word") gives each word in the text
lemma = Fs("lemma").v(word) # get the lemma for the current word
# Check if the lemma is already in the dictionary
if lemma in lemmaInfoDict:
# Increment the count of the lemma
lemmaInfoDict[lemma] = (lemmaInfoDict[lemma][0] + 1, lemmaInfoDict[lemma][1])
else:
# If it's a new lemma, set the count to 1 and record the current word node as the first occurrence
lemmaInfoDict[lemma] = (1, word)
Note that this cell will not provide any output since it just creates the dictitonary lemmaInfoDict which will be used in the next section.
The just created dictionary lemmaInfoDict contains the frequency of all lemmata. To print only those with a count of 1, you can use the following script:
from IPython.display import display
import pandas as pd
# Filter lemmas with a count of 1 (hapax legomena) and retrieve section information
hapaxLegomena = {
lemma: T.sectionFromNode(firstOccurrence)
for lemma, (count, firstOccurrence) in lemmaInfoDict.items()
if count == 1
}
# Convert the dictionary to a DataFrame, splitting the tuple into separate columns
hapaxLegomenaDF = pd.DataFrame.from_dict(
hapaxLegomena,
orient='index',
columns=['Book', 'Chapter', 'Verse']
)
# Display the DataFrame as a table
display(hapaxLegomenaDF)
| Book | Chapter | Verse | |
|---|---|---|---|
| Ζάρα | Matthew | 1 | 3 |
| Θαμάρ | Matthew | 1 | 3 |
| Ῥαχάβ | Matthew | 1 | 5 |
| Ῥούθ | Matthew | 1 | 5 |
| Οὐρίας | Matthew | 1 | 6 |
| ... | ... | ... | ... |
| δωδέκατος | Revelation | 21 | 20 |
| ἀμέθυστος | Revelation | 21 | 20 |
| διαυγής | Revelation | 21 | 21 |
| κατάθεμα | Revelation | 22 | 3 |
| ῥυπαίνω | Revelation | 22 | 11 |
1927 rows × 3 columns
To map each lemma to its Louw-Nida top-level classification, we first need to create a mapping dictionary called louwNidaMapping (section 3.3.1). Next, we can use the first part of the value from the ln feature as an index in the louwNidaMapping table. This will "translate" the Louw-Nida top-level domain from a numeric value to a more descriptive one. This approach allows us to group all hapax legomena by their top-level Louw-Nida domain (the primary domain number before any subdomains). Later in the script, we add links to BibleOL to facilitate easy verification of each occurrence’s context.
# The following script will produce a dictionairy of Louw-Nida Top-level domains
# The structure of the dictionairy is:
# louwNidaMapping = {
# numeric (key) : "description"
# ...
# }
import requests
from bs4 import BeautifulSoup
# Debugging mode (False, True)
debug = False
# URL of the Louw-Nida classifications page
url = "https://www.laparola.net/greco/louwnida.php"
# Retrieve the webpage content
response = requests.get(url)
if debug:
print(f"Retrieving URL {url} returned {response}")
response.raise_for_status() # Check for request errors
# Parse the HTML content with BeautifulSoup
soup = BeautifulSoup(response.text, "html.parser")
# Initialize an empty dictionary
louwNidaMapping = {}
# Find all <h3> elements that contain the Louw-Nida classification data
for entry in soup.find_all("h3"):
# Extract the number from the <a> tag within the <h3> tag
numberTag = entry.find("a")
descriptionText = entry.get_text(strip=True)
# Ensure there's content to process
if numberTag and descriptionText:
# Attempt to parse the number and description
keyText = numberTag.get_text(strip=True)
try:
# Convert the number to an integer
key = int(keyText)
except ValueError:
# If conversion fails, skip this entry
if debug:
print(f"Skipping entry due to non-numeric key: {keyText}")
continue
# Get description by removing the number portion from the full text
description = descriptionText.replace(keyText, "", 1).strip(' "')
# Add to dictionary
louwNidaMapping[key] = description
if debug:
print(f"Added classification: {key}: {description}")
if debug:
print(f"Resulting dictionary: {louwNidaMapping}")
The following script will group the hapax legomena according to its Louw-Nida top-level clasification. Code has been added to allow for 'drilling down' by collapsing and expanding parts of the output.
from IPython.display import display, HTML # IPython is included as part of the standard environment in Jupyter notebooks
from collections import defaultdict
# Initialize dictionary for lemma occurrences and their first occurrence nodes
lemmaInfoDict = {}
# Iterate over each word in the Greek New Testament
for word in F.otype.s("word"): # fetch each word node in the text
lemma = F.lemma.v(word) # get the lemma associated with the current word
# Check if the lemma is already in lemmaInfoDict
if lemma in lemmaInfoDict:
# Increment the count of the lemma
lemmaInfoDict[lemma] = (lemmaInfoDict[lemma][0] + 1, lemmaInfoDict[lemma][1])
else:
# If lemma is new, set count to 1 and record the current word as the first occurrence
lemmaInfoDict[lemma] = (1, word)
# Initialize dictionary to store hapax legomena grouped by Louw-Nida classifications
hapaxLouwNidaTopLevelDict = defaultdict(list)
# Filter lemmas that are hapax legomena and group them by Louw-Nida domain
for lemma, (count, firstOccurrenceNode) in lemmaInfoDict.items():
if count == 1: # Only consider hapax legomena (occurrences = 1)
# Get Louw-Nida classification, replace None with "0"
louwNida = str(F.ln.v(firstOccurrenceNode) or "0")
book, chapter, verse = T.sectionFromNode(firstOccurrenceNode) # Get location details
topLevelLouwNidaClass = louwNida.split(".")[0] # Extract top-level domain or "0" if missing
# Add lemma with location details to the corresponding Louw-Nida classification group
hapaxLouwNidaTopLevelDict[topLevelLouwNidaClass].append((lemma, book, chapter, verse, firstOccurrenceNode))
# Calculate totals for each Louw-Nida top-level classification group
louwNidaTotals = {group: len(lemmas) for group, lemmas in hapaxLouwNidaTopLevelDict.items()}
# Sort totals by total count from high to low
sortedLouwNidaTotals = sorted(louwNidaTotals.items(), key=lambda x: x[1], reverse=True)
# Generate HTML output with collapsible sections for each Louw-Nida classification
hapaxHtmlOutput = "<html><body><h2>Hapax legomena by Louw-Nida top-level classification</h2>\n"
for group, total in sortedLouwNidaTotals:
# Display "Unknown" for group "0"
classification_label = "Unknown" if group == "0" else louwNidaMapping.get(int(group), 'Unknown Classification')
hapaxHtmlOutput += f"<details><summary>Louw-Nida Top-Level Domain {group}: {classification_label} (Total: {total})</summary><ul>\n"
for lemma, book, chapter, verse, node in hapaxLouwNidaTopLevelDict[group]:
hapaxHtmlOutput += f"<li><a href='https://learner.bible/text/show_text/nestle1904/{book}/{chapter}/{verse}' target='_blank'>{book} {chapter}:{verse}</a>:\t{lemma} ({F.gloss.v(node)})</li>\n"
hapaxHtmlOutput += "</ul></details>\n"
hapaxHtmlOutput += "</body></html>"
# Display the report in the Jupyter Notebook
display(HTML(hapaxHtmlOutput))
The following script creates a download link for the report generated in the previous cell.
from IPython.display import HTML
import base64 # used to encode the data to be downloaded
def createDownloadLink(htmlContent, fileName):
# Encode the HTML content to base64
b64Html = base64.b64encode(htmlContent.encode()).decode()
# Create the HTML download link
downloadLink = f'''
<a download="{fileName}" href="data:text/html;base64,{b64Html}">
<button>Download HTML File</button>
</a>
'''
return HTML(downloadLink)
# Display the download link in the notebook
createDownloadLink(hapaxHtmlOutput, 'hapax_to_louw_nida.html')
1 See the extensive discussion on ἐπιούσιος in: Brant Pitre, Jesus and the Jewish Roots of the Eucharist, Unlocking the Secrets of the Last Supper (New York: Doubleday, 2011), 93-96; pasim.
2 See Arnold E. Clinton (general editor), Zondervan Illustrated Bible Backgrounds Commentary, Volume Three, Romans to Philemon (Grand Rapids, MI: Zondervan Academic, 2002). Note that 4 Maccabees is included in LXX-Vaticanus.
3 See for example Kittel, ThWNT IV 285f. In EKK, Wolfgang Schrage cautions that, although numerous papyri link this word to a sacred monetary collection, this does not confirm that Paul's usage necessarily includes a cultic-sacral component. Wolfgang Schrage, Der erste Letter an die Korinther VII/4, Evangelisch Katholischer Komentar zum neuen Testament (Düsseldorf: Benziger, 2001), 425.
4 Johannes P. Louw and Eugene Albert Nida, Greek-English Lexicon of the New Testament: Based on Semantic Domains (New York: United Bible Societies, 1996).
5 The dictionary is based upon Louw-Nida Lexicon @ laparola.net.
The scripts in this notebook require (beside text-fabric) the following Python libraries to be installed in the environment:
base64
collections
pandas
subprocess
You can install any missing library from within Jupyter Notebook using eitherpip or pip3.
| Author | Tony Jurg |
| Version | 1.1 |
| Date | 17 November 2024 |
The following cell displays the active Anaconda environment along with a list of all installed packages and their versions within that environment.
import subprocess
from IPython.display import display, HTML
# Display the active conda environment
!conda env list | findstr "*"
# Run conda list and capture the output
condaListOutput = subprocess.check_output("conda list", shell=True).decode("utf-8")
# Wrap the output with <details> and <summary> HTML tags
htmlOutput = "<details><summary>Click to view installed packages</summary><pre>"
htmlOutput += condaListOutput
htmlOutput += "</pre></details>"
# Display the HTML in the notebook
display(HTML(htmlOutput))
Text-Fabric * C:\Users\tonyj\anaconda3\envs\Text-Fabric
# packages in environment at C:\Users\tonyj\anaconda3\envs\Text-Fabric: # # Name Version Build Channel anaconda_prompt 1.1.0 haa95532_0 anyio 4.6.2.post1 pyhd8ed1ab_0 conda-forge argon2-cffi 23.1.0 pyhd8ed1ab_0 conda-forge argon2-cffi-bindings 21.2.0 py312h4389bb4_5 conda-forge arrow 1.3.0 pyhd8ed1ab_0 conda-forge asttokens 2.4.1 pyhd8ed1ab_0 conda-forge async-lru 2.0.4 pyhd8ed1ab_0 conda-forge attrs 24.2.0 pyh71513ae_0 conda-forge babel 2.14.0 pyhd8ed1ab_0 conda-forge beautifulsoup4 4.12.3 pyha770c72_0 conda-forge bleach 6.1.0 pyhd8ed1ab_0 conda-forge blinker 1.8.2 pypi_0 pypi bokeh 3.6.0 pypi_0 pypi brotli-python 1.1.0 py312h275cf98_2 conda-forge bzip2 1.0.8 h2466b09_7 conda-forge ca-certificates 2024.8.30 h56e8100_0 conda-forge cached-property 1.5.2 hd8ed1ab_1 conda-forge cached_property 1.5.2 pyha770c72_1 conda-forge certifi 2024.8.30 pyhd8ed1ab_0 conda-forge cffi 1.17.1 py312h4389bb4_0 conda-forge charset-normalizer 3.4.0 pyhd8ed1ab_0 conda-forge click 8.1.7 pypi_0 pypi colorama 0.4.6 pyhd8ed1ab_0 conda-forge comm 0.2.2 pyhd8ed1ab_0 conda-forge console_shortcut 0.1.1 haa95532_6 contourpy 1.3.0 pypi_0 pypi cpython 3.12.7 py312hd8ed1ab_0 conda-forge cryptography 43.0.3 pypi_0 pypi cycler 0.12.1 pypi_0 pypi debugpy 1.8.7 py312h275cf98_0 conda-forge decorator 5.1.1 pyhd8ed1ab_0 conda-forge defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge deprecated 1.2.14 pypi_0 pypi entrypoints 0.4 pyhd8ed1ab_0 conda-forge et-xmlfile 2.0.0 pypi_0 pypi exceptiongroup 1.2.2 pyhd8ed1ab_0 conda-forge executing 2.1.0 pyhd8ed1ab_0 conda-forge flask 3.0.3 pypi_0 pypi fonttools 4.54.1 pypi_0 pypi fqdn 1.5.1 pyhd8ed1ab_0 conda-forge h11 0.14.0 pyhd8ed1ab_0 conda-forge h2 4.1.0 pyhd8ed1ab_0 conda-forge hpack 4.0.0 pyh9f0ad1d_0 conda-forge httpcore 1.0.6 pyhd8ed1ab_0 conda-forge httpx 0.27.2 pyhd8ed1ab_0 conda-forge hyperframe 6.0.1 pyhd8ed1ab_0 conda-forge idna 3.10 pyhd8ed1ab_0 conda-forge importlib-metadata 8.5.0 pyha770c72_0 conda-forge importlib_metadata 8.5.0 hd8ed1ab_0 conda-forge importlib_resources 6.4.5 pyhd8ed1ab_0 conda-forge intel-openmp 2024.2.1 h57928b3_1083 conda-forge ipykernel 6.29.5 pyh4bbf305_0 conda-forge ipython 8.28.0 pyh7428d3b_0 conda-forge isoduration 20.11.0 pyhd8ed1ab_0 conda-forge itsdangerous 2.2.0 pypi_0 pypi jedi 0.19.1 pyhd8ed1ab_0 conda-forge jinja2 3.1.4 pyhd8ed1ab_0 conda-forge json5 0.9.25 pyhd8ed1ab_0 conda-forge jsonpointer 3.0.0 py312h2e8e312_1 conda-forge jsonschema 4.23.0 pyhd8ed1ab_0 conda-forge jsonschema-specifications 2024.10.1 pyhd8ed1ab_0 conda-forge jsonschema-with-format-nongpl 4.23.0 hd8ed1ab_0 conda-forge jupyter-lsp 2.2.5 pyhd8ed1ab_0 conda-forge jupyter_client 8.6.3 pyhd8ed1ab_0 conda-forge jupyter_core 5.7.2 pyh5737063_1 conda-forge jupyter_events 0.10.0 pyhd8ed1ab_0 conda-forge jupyter_server 2.14.2 pyhd8ed1ab_0 conda-forge jupyter_server_terminals 0.5.3 pyhd8ed1ab_0 conda-forge jupyterlab 4.2.5 pyhd8ed1ab_0 conda-forge jupyterlab_pygments 0.3.0 pyhd8ed1ab_1 conda-forge jupyterlab_server 2.27.3 pyhd8ed1ab_0 conda-forge kiwisolver 1.4.7 pypi_0 pypi krb5 1.21.3 hdf4eb48_0 conda-forge libblas 3.9.0 24_win64_mkl conda-forge libcblas 3.9.0 24_win64_mkl conda-forge libexpat 2.6.3 he0c23c2_0 conda-forge libffi 3.4.2 h8ffe710_5 conda-forge libhwloc 2.11.1 default_h8125262_1000 conda-forge libiconv 1.17 hcfcfb64_2 conda-forge liblapack 3.9.0 24_win64_mkl conda-forge libsodium 1.0.20 hc70643c_0 conda-forge libsqlite 3.46.1 h2466b09_0 conda-forge libxml2 2.12.7 h0f24e4e_4 conda-forge libzlib 1.3.1 h2466b09_2 conda-forge markdown 3.7 pypi_0 pypi markdown2 2.5.1 pypi_0 pypi markupsafe 3.0.1 py312h31fea79_1 conda-forge matplotlib 3.9.2 pypi_0 pypi matplotlib-inline 0.1.7 pyhd8ed1ab_0 conda-forge mistune 3.0.2 pyhd8ed1ab_0 conda-forge mkl 2024.1.0 h66d3029_694 conda-forge mplcursors 0.5.3 pypi_0 pypi nbclient 0.10.0 pyhd8ed1ab_0 conda-forge nbconvert-core 7.16.4 pyhd8ed1ab_1 conda-forge nbformat 5.10.4 pyhd8ed1ab_0 conda-forge nest-asyncio 1.6.0 pyhd8ed1ab_0 conda-forge notebook 7.2.2 pyhd8ed1ab_0 conda-forge notebook-shim 0.2.4 pyhd8ed1ab_0 conda-forge numpy 2.1.2 py312hf10105a_0 conda-forge openpyxl 3.1.5 pypi_0 pypi openssl 3.3.2 h2466b09_0 conda-forge overrides 7.7.0 pyhd8ed1ab_0 conda-forge packaging 24.1 pyhd8ed1ab_0 conda-forge pandas 2.2.3 py312h72972c8_1 conda-forge pandocfilters 1.5.0 pyhd8ed1ab_0 conda-forge parso 0.8.4 pyhd8ed1ab_0 conda-forge pickleshare 0.7.5 py_1003 conda-forge pillow 11.0.0 pypi_0 pypi pip 24.2 pyh8b19718_1 conda-forge pkgutil-resolve-name 1.3.10 pyhd8ed1ab_1 conda-forge platformdirs 4.3.6 pyhd8ed1ab_0 conda-forge plotly 5.24.1 pypi_0 pypi prometheus_client 0.21.0 pyhd8ed1ab_0 conda-forge prompt-toolkit 3.0.48 pyha770c72_0 conda-forge psutil 6.0.0 py312h4389bb4_2 conda-forge pthreads-win32 2.9.1 h2466b09_4 conda-forge pure_eval 0.2.3 pyhd8ed1ab_0 conda-forge pycparser 2.22 pyhd8ed1ab_0 conda-forge pygithub 2.4.0 pypi_0 pypi pygments 2.18.0 pyhd8ed1ab_0 conda-forge pyjwt 2.9.0 pypi_0 pypi pynacl 1.5.0 pypi_0 pypi pyparsing 3.2.0 pypi_0 pypi 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