Developed robust web scraping and NLP workflows

• Day: 2024-07-11
• Time: 20:00 to 20:45
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Web Scraping, NLP, Error Handling, Entity Recognition, BERT

Description

Session Goal: The session aimed to enhance web scraping and natural language processing (NLP) workflows, focusing on robustness, error handling, and entity recognition accuracy.

Key Activities:

• Developed a comprehensive workflow for web scraping and NLP, utilizing tools like BeautifulSoup, Scrapy, spaCy, and Pandas.
• Implemented error handling in web scraping functions to manage SSL and HTTP exceptions, ensuring robustness.
• Updated the clean_html function to improve data cleaning by removing unnecessary whitespace.
• Enhanced entity recognition accuracy through preprocessing, custom model training, and post-processing techniques.
• Explored the use of spaCy’s Spanish models for NLP tasks, including entity extraction.
• Developed a structured approach for text classification using machine learning, specifically with Scikit-Learn and BERT models.

Achievements:

• Successfully created robust web scraping scripts with comprehensive error handling.
• Improved data cleaning processes and entity recognition workflows.
• Established a foundation for text classification using advanced machine learning models.

Pending Tasks:

• Further fine-tuning of the BERT-based text classification model for improved performance.
• Integration of the enhanced NLP workflows into existing systems for real-world application.

Evidence

• source_file=2024-07-11.sessions.jsonl, line_number=3, event_count=0, session_id=7552c1c708cd51277fcb6b726d82cadd4b76336f08e70260fff2ee61ad837f0b
• event_ids: []