📅 2023-12-26 — Session: Implemented NLP for Legal Text Analysis in Python
🕒 21:00–21:45
🏷️ Labels: Python, NLP, Spacy, Data Extraction, Legal Text
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal: The session aimed to develop and refine Python scripts for extracting resolutions, dispositions, and linguistic elements from legal summaries, focusing on improving natural language processing (NLP) tasks.
Key Activities:
- Developed Python scripts using regex for extracting resolutions and dispositions from summaries.
- Addressed issues with downloading Spanish stopwords due to connectivity, proposing manual word frequency analysis.
- Implemented a Python function to remove stopwords and convert text to uppercase for better word frequency analysis.
- Utilized spaCy for extracting verbs and objects from legal texts, integrating these functions into a broader data extraction process.
- Discussed challenges with spaCy’s language model in accurately tagging verbs and objects, suggesting text modifications for improved NLP accuracy.
Achievements:
- Successfully implemented regex-based extraction of resolutions and dispositions.
- Developed a workaround for stopword filtering in the absence of connectivity.
- Integrated spaCy for advanced NLP tasks, enhancing the ability to analyze legal texts.
Pending Tasks:
- Further refine the spaCy language model to improve the accuracy of verb and object extraction in legal documents.
- Explore additional methods to enhance text preprocessing for better NLP outcomes.