If you’ve ever dealt with public-sector data, you know the pain. It’s often locked away in the most user-unfriendly format imaginable: the PDF.
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I recently found myself facing a mountain of these. Specifically, hundreds of special education due process hearing decisions from the Texas Education Agency. Each document was a dense, multi-page legal decision. My goal was simple: figure out who won each case—the “Petitioner” (usually the parent) or the “Respondent” (the school district).
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Reading them all manually would have taken weeks. The data was there, but it was unstructured, inconsistent, and buried in legalese. I knew I could automate this. What started as a simple script evolved into a full-fledged data engineering and NLP pipeline that can process a decade’s worth of legal decisions in minutes.
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Here’s how I did it.
The Game Plan: An ETL Pipeline for Legal Text
ETL (Extract, Transform, Load) is usually for databases, but the concept fits perfectly here:
- Extract: Build a web scraper to systematically download every PDF decision from the government website and rip the raw text out of it.
- Transform: This is the magic. Build an NLP engine that can read the unstructured text, understand the context, and classify the outcome of the case.
- Load: Save the results into a clean, structured CSV file for easy analysis.
Step 1: The Extraction – Conquering the PDF Mountain
First, I needed the data. The TEA website hosts decisions on yearly pages, so the first script, texasdueprocess_extract.py, had to be a resilient scraper. I used a classic Python scraping stack:
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- requests and BeautifulSoup4 to parse the HTML of the index pages and find all the links to the PDF files.
- PyPDF2 to handle the PDFs themselves.
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A key insight came early: the most important parts of these documents are always at the end—the “Conclusions of Law” and the “Orders.” Scraping the full 50-page text for every document would be slow and introduce a lot of noise. So, I optimized the scraper to only extract text from the last two pages.
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texasdueprocess_extract.py – Snippet
# A look inside the PDF extraction logic
import requests
import PyPDF2
import io
def extract_text_from_pdf(url):
try:
response = requests.get(url)
pdf_file = io.BytesIO(response.content)
pdf_reader = PyPDF2.PdfReader(pdf_file)
text = ""
# Only process the last two pages to get the juicy details
for page_num in range(len(pdf_reader.pages))[-2:]:
page = pdf_reader.pages[page_num]
text += page.extract_text()
return text
except Exception as e:
print(f"Error processing {url}: {e}")
return None
This simple optimization made the extraction process much faster and more focused. The script iterated through years of decisions, saving the extracted text into a clean JSON file, ready for analysis.
Step 2: The Transformation – Building a Legal “Brain”
This was the most challenging and interesting part. How do you teach a script to read and understand legal arguments?
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My first attempt (examineeddata.py) was naive. I used NLTK to perform n-gram frequency analysis, hoping to find common phrases. It was interesting but ultimately useless. “Hearing officer” was a common phrase, but it told me nothing about who won.
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I needed rules. I needed a domain-specific classifier. This led to the final script, examineeddata_2.py, which is built on a few key principles.
A. Isolate the Signal with Regex
Just like in the scraper, I knew the “Conclusions of Law” and “Orders” sections were the most important. I used a robust regular expression to isolate these specific sections from the full text.
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examineeddata_2.py – Regex for Sectional Analysis
# This regex looks for "conclusion(s) of law" and captures everything
# until it sees "order(s)", "relief", or another section heading.
conclusions_match = re.search(
r"(?:conclusion(?:s)?\s+of\s+law)(.+?)(?:order(?:s)?|relief|remedies|viii?|ix|\bbased upon\b)",
text, re.DOTALL | re.IGNORECASE | re.VERBOSE)
# This one captures everything from "order(s)" or "relief" to the end of the doc.
orders_match = re.search(
r"(?:order(?:s)?|relief|remedies)(.+)$",
text, re.DOTALL | re.IGNORECASE | re.VERBOSE
)
conclusions = conclusions_match.group(1).strip() if conclusions_match else ""
orders = orders_match.group(1).strip() if orders_match else ""
This allowed me to analyze the most decisive parts of the text separately and even apply different weights to them later.
B. Curated Keywords and Stemming
Next, I created two lists of keywords and phrases that strongly indicated a win for either the Petitioner or the Respondent. This required some domain knowledge.
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- Petitioner Wins: “relief requested…granted”, “respondent failed”, “order to reimburse”
- Respondent Wins: “petitioner failed”, “relief…denied”, “dismissed with prejudice”
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But just matching strings isn’t enough. Legal documents use variations of words (“grant”, “granted”, “granting”). To solve this, I used NLTK’s PorterStemmer to reduce every word in both my keyword lists and the document text to its root form.
from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
# Now "granted" becomes "grant", "failed" becomes "fail", etc.
stemmed_keyword = stemmer.stem("granted")
This made the matching process far more effective.
C. The Secret Sauce: Negation Handling
This was the biggest “gotcha.” Finding the keyword “fail” is great, but the phrase “did not fail to comply” completely flips the meaning. A simple keyword search would get this wrong every time.
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I built a negation-aware regex that specifically looks for words like “not,” “no,” or “failed to” appearing before a keyword.
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examineeddata_2.py – Negation Logic
For each keyword, build a negation-aware regex
keyword = "complied"
negated_keyword = r"\b(?:not|no|fail(?:ed)?\s+to)\s+" + re.escape(keyword) + r"\b"
First, check if the keyword exists
if re.search(rf"\b{keyword}\b", text_section):
# THEN, check if it's negated
if re.search(negated_keyword, text_section):
# This is actually a point for the OTHER side!
petitioner_score += medium_weight
else:
# It's a normal, positive match
respondent_score += medium_weight
This small piece of logic dramatically increased the accuracy of the classifier.
Step 2: The Transformation – Building a Legal “Brain”
Finally, I put it all together in a scoring system. I assigned different weights to keywords and gave matches found in the “Orders” section a 1.5x multiplier, since an order is a definitive action.
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The script loops through every case file, runs the analysis, and determines a winner: “Petitioner,” “Respondent,” “Mixed” (if both scored points), or “Unknown.” The output is a simple, clean `decision_analysis.csv` file.
| docket | winner | petitioner_score | respondent_score |
| :--- | :--- | :--- | :--- |
| 001-SE-1023 | Respondent | 1.0 | 7.5 |
| 002-SE-1023 | Petitioner | 9.0 | 2.0 |
| 003-SE-1023 | Mixed | 3.5 | 4.0 |
A quick `df['winner'].value_counts()` in Pandas gives me the instant summary I was looking for.
Final Thoughts
This project was a powerful reminder that you don’t always need a massive, multi-billion-parameter AI model to solve complex NLP problems. For domain-specific tasks, a well-crafted, rule-based system with clever heuristics can be incredibly effective and efficient. By breaking down the problem—isolating text, handling word variations, and understanding negation, I was able to turn a mountain of messy PDFs into a clean, actionable dataset. \n
