Developed Automated News Article Processing Pipeline
- Day: 2024-06-09
- Time: 16:30 to 18:30
- Project: Media
- Workspace: WP 2: Operational
- Status: Completed
- Priority: MEDIUM
- Assignee: Matías Nehuen Iglesias
- Tags: News Processing, Automation, NLP, Data Extraction, BERT
Description
Session Goal: The session aimed to develop a comprehensive automated pipeline for processing news articles, including scraping, extraction, classification, and analysis.
Key Activities:
- Explored techniques for summarizing news articles and extracting relevant information using both extractive and abstractive methods.
- Planned and set up workflows for advanced article extraction using Newspaper3k and database integration for structured and unstructured data.
- Executed initial workflows for news scraping and extraction, with placeholders for NLP tasks.
- Analyzed article titles related to Argentine politics, categorizing them into themes like economic policies and government actions.
- Proposed and refined an article classification system using BERT, with steps for storing results in BigQuery.
- Addressed Python library warnings and resolved import errors for BERT model deployment.
- Managed disk space using Linux commands and handled DataFrame text classification errors.
- Fine-tuned BERT for sequence classification, providing installation and usage guidance.
Achievements:
- Successfully set up an automated pipeline for news article processing, including scraping, extraction, classification, and analysis.
- Resolved technical issues related to Python libraries and model deployment.
Pending Tasks:
- Implement entity recognition and summarization enhancements in the pipeline.
- Continue refining classification models and workflows for better accuracy and efficiency.
Evidence
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- event_ids: []