Enhanced NLP Pipeline and Keyword Extraction

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📅 2025-02-17 — Session: Enhanced NLP Pipeline and Keyword Extraction

🕒 16:00–16:30
🏷️ Labels: NLP, RAKE, Tfidfvectorizer, Python, Keyword Extraction
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The goal of this session was to resolve issues with NLP text processing and keyword extraction, specifically focusing on optimizing the TfidfVectorizer and RAKE methods.

Key Activities

  • Resolving TfidfVectorizer Error: Addressed an error related to the stop_words parameter in TfidfVectorizer by converting a set of stop words into a suitable list format for scikit-learn.
  • Streamlining NLP Pipeline: Developed a more efficient and readable NLP text processing script, including sections for loading, preprocessing, topic extraction, and saving results.
  • Optimizing RAKE Method: Analyzed the RAKE keyword extraction method, identifying verbosity issues and suggesting improvements for more concise keyword extraction.
  • Adjusting RAKE Parameters: Modified RAKE parameters to improve keyword relevance, including filtering thresholds, phrase length, and stopword management.

Achievements

  • Successfully resolved the TfidfVectorizer stop words error.
  • Implemented a streamlined NLP processing pipeline.
  • Enhanced RAKE keyword extraction method for better efficiency and relevance.

Pending Tasks

  • Further testing and validation of the adjusted RAKE parameters to ensure optimal performance.

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