Processing Mishnah Text with Python: Cleaning, Formatting, and Enhancing Readability

Working with Hebrew text in Python can present unique challenges, particularly when it comes to handling diacritical marks (nikud), adjusting punctuation, and formatting citations.¹

In this piece, we’ll dive into a Python script that preprocesses Hebrew text for enhanced readability and consistency. 

This script is designed to clean up Hebrew text, refine punctuation, and apply custom formatting rules—a useful tool for researchers, translators, or anyone working with Hebrew-language data. 

On splitting the mishnah into sections by sentence or phrase

One significant advantage of digital texts is that they are not restricted by space limitations, enabling line breaks and other user-friendly formatting to enhance readability without worrying about space constraints.

It’s clear that the Mishnah would greatly benefit from being divided into much smaller sections, akin to biblical verses or sentences. Since the Mishnah’s primary unit is the casuistic sentence—structured as "In case/scenario X, the law is Y"—each of these sentences should be treated as a distinct "verse" or segment. This would resemble Sefaria’s Bible layout, where each verse, roughly equivalent to a single sentence, stands alone as a new section.

Splitting the Mishnah into their component sentences makes it much easier to navigate and analyze, especially since—as just mentioned—each casuistic phrase is often a complete, self-contained legal ruling. Such segmentation also reveals patterns, themes, and rhetorical techniques that are much harder to identify in the traditional structure. This approach could open up new ways to study the Mishnah's literary style.²

Outline

1. Why Preprocess Hebrew Text?

2. Breaking Down the Code

   1. Removing nikud (Diacritics)

   2. Splitting and Limiting Text Sections

   3. Processing Each Section: Punctuation and Formatting

   4. Outputting the Processed Text

3. Final Thoughts

4. Appendix # 1 - Initial Input Text, vs. Final Output Text

   1. Initial text, as displayed at Sefaria website

   2. Output text, after processing, as displayed in a Google doc

5. Appendix #2 - Final Code

Why Preprocess Hebrew Text?

When working with Hebrew texts, especially ancient or religious ones, raw text often comes with diacritics (nikud), inconsistent punctuation, and formatting that doesn’t always align with modern standards. Nikud, while helpful in certain contexts, can clutter analysis or lead to matching errors in natural language processing tasks. Moreover, standardized punctuation can help make the text more readable and allow consistent structural interpretation.

Our goal here is to:

1. Remove diacritical marks to simplify the text.

2. Refine punctuation based on certain keywords.

3. Apply specific formatting rules to sections of the text.

4. Print out the processed text for further analysis or display.

Let’s dive into the details of the script.

Breaking Down the Code

1. Removing nikud (Diacritics)

The script starts with a function to remove nikud. Hebrew diacritics are found within the Unicode range \u0591 to \u05C7. Removing them is straightforward with regular expressions:

import re

def remove_nikud(text):
    return re.sub(r'[\u0591-\u05C7]', '', text)

This line of code is essential when working with Hebrew texts where vowels are not necessary for comprehension or are too detailed for the analysis you’re conducting.

2. Splitting and Limiting Text Sections

After reading in the text file, the script splits the content into sections based on periods (.). (Here, only the first 50 sections are retained to limit the data load, making the process faster and more manageable, at least for the initial testing phase):

master_filename = "/content/master_text_file.txt"
with open(master_filename, 'r', encoding='utf-8') as f:
    text = f.read()
sections = text.split('.')
sections = sections[:50]

3. Processing Each Section: Punctuation and Formatting

This part of the script applies custom transformations to each section. Let’s go through each one:

• Adding Colons After Key Terms:
  Hebrew texts often use phrases like 'אומר' ("says") and 'אומרים' ("they say"). When followed by a comma, this script replaces the comma with a colon (:), enhancing readability:

section = re.sub(r'(אומר|אומרים),', r'\1:', section)

• Formatting Biblical Citations:
  When biblical citations follow the term 'שנאמר' ("as it was said"), we want to ensure a consistent format. Here, any parentheses that follow are adjusted so that 'שנאמר' is followed by a colon:

section = re.sub(
    r'(שנאמר)(\s*)\((.*?)\)',
    lambda m: f"{m.group(1)}:\"({m.group(3)})",
    section
)

• Adding Question Marks for Certain Queries:
  Hebrew texts often start questions with 'כיצד' ("how") or 'מה בין' ("what between"), and ensuring these lines end with a question mark (?) improves clarity. This check adds a question mark if it’s not already there:

  question_pattern = r'^(כיצד|מה בין)(.*)'
  if re.match(question_pattern, section):
      if not section.strip().endswith('?'):
          section = section.rstrip(':\u05C3 \t\n\r') + '?'

• Removing Trailing Colons and Similar Marks:
  To prevent any extraneous colons from appearing at the end of lines, the script strips them out if present:

  section = re.sub(r'[:\u05C3]\s*$', '', section, flags=re.MULTILINE)

This careful punctuation management ensures that Hebrew texts are consistent and visually cleaner for reading or further processing.

4. Outputting the Processed Text

After processing, the script simply prints each section:

for processed_section in processed_sections:

    print(processed_section)

This output could be redirected to a file or used directly in further analysis steps, depending on the needs of the user.

Final Thoughts

This script serves as a powerful example of how Python can be leveraged to process Hebrew text, refine formatting, and standardize punctuation. Whether you’re a researcher preparing Hebrew data for analysis or a translator aiming to clean up text, such preprocessing steps can significantly improve readability and consistency in your final output.

As you work with Hebrew text or any language with unique orthographic rules, consider customizing scripts like this one to handle specific linguistic quirks. With Python’s regular expression capabilities and Unicode support, you’re equipped to tackle a wide range of text-processing challenges.

Appendix # 1 - Initial Input Text, vs. Final Output Text

Initial text, as displayed at Sefaria website

Text: Arakhin Chapter 1 (Mishnah_Arakhin.1.1-4).

Screenshot:

Output text, after processing, as displayed in a Google doc

Screenshot:³

Appendix #2 - Final Code

import re

def remove_nikud(text):
    return re.sub(r'[\u0591-\u05C7]', '', text)

def print_last_chars(section):
    print("Last characters and their Unicode code points:")
    for idx, char in enumerate(section[-5:], 1):
        print(f"Char {idx}: '{char}' (Unicode: {ord(char)})")

# Read the master text file
master_filename = "/content/master_text_file.txt"
with open(master_filename, 'r', encoding='utf-8') as f:
    text = f.read()

# Split into sections by period ('.')
sections = text.split('.')

# Limit to the first 50 sections
sections = sections[:50]

processed_sections = []

for section in sections:
    section = section.strip()

    # Remove Nikud (diacritics) from the text
    section = remove_nikud(section)

    # Add colon after 'אומר' or 'אומרים', replacing any following comma
    section = re.sub(r'(אומר|אומרים),', r'\1:', section)

    # Modify 'שנאמר' with biblical citations throughout the section
    section = re.sub(
        r'(שנאמר)(\s*)\((.*?)\)',
        lambda m: f"{m.group(1)}:\"({m.group(3)})",
        section
    )

    # Add question mark '?' at the end of lines that start with 'כיצד' or 'מה בין'
    question_pattern = r'^(כיצד|מה בין)(.*)'
    if re.match(question_pattern, section):
        # Add question mark if not already present
        if not section.strip().endswith('?'):
            section = section.rstrip(':\u05C3 \t\n\r') + '?'

    # Remove colon (':' or '׃') from the end of each line in the section
    section = re.sub(r'[:\u05C3]\s*$', '', section, flags=re.MULTILINE)

    processed_sections.append(section)


# Output the processed sections
for processed_section in processed_sections:
    print(processed_section)

1. This is part of my ongoing project on Mishnah formatting and analysis of its structure, see my relevant pieces at my Academia page, section Mishnah formatting, and see yesterday’s piece, on the same topic. See yesterday’s piece for how to retrieve Mishnah text from Sefaria via their API.

See also my previous pieces on programmatically processing Talmud text for readability, linked to in my index here, section: “Digital Layout of the Talmud and other Rabbinic Texts“

2. Breaking text into sentence- or phrase-based sections is a technique I also use in my pieces on Talmud, as do Menachem Katz and others in their formatting approaches. On this, see my Seforim blog article, "Pixel."

Structuring the Talmud this way reduces the overwhelming feeling of a "sea" one might get lost in (playing on the familiar phrase "sea of Talmud").

3. Technical:

• Font: Frank Ruhl Libre - Medium, 13 point

• Paragraph formatting: space after each paragraph