Optimized Text Processing and Memory Management

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📅 2025-02-17 — Session: Optimized Text Processing and Memory Management

🕒 15:20–16:05
🏷️ Labels: Text_Processing, Memory_Management, Python, TF-IDF, Spacy, NLTK
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to enhance text processing capabilities and manage memory issues in Python, focusing on keyword extraction, stopword handling, and memory optimization.

Key Activities

  • Explored strategies for filtering and selecting keywords using TF-IDF and LDA.
  • Implemented Spanish stopwords in TfidfVectorizer using NLTK and spaCy.
  • Optimized text processing workflows and regex for text cleaning.
  • Addressed memory issues in Python with spaCy and NLTK, providing solutions for common errors.

Achievements

  • Developed a comprehensive guide for keyword extraction and text processing.
  • Improved the efficiency and clarity of text processing notebooks.
  • Successfully implemented custom stopword lists in TfidfVectorizer.
  • Provided solutions for memory management and error resolution in Python.

Pending Tasks

  • Further testing of optimized text processing workflows.
  • Continuous monitoring and adjustment of memory management strategies.

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