📅 2024-09-11 — Session: Developed NLP Entity and Relation Extraction Functions
🕒 00:10–23:20
🏷️ Labels: NLP, Python, Entity Extraction, Relation Extraction, Spacy, Legal Text
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The goal of this session was to enhance natural language processing (NLP) capabilities by developing and improving functions for entity and relation extraction, particularly focusing on legal texts and complex sentence structures.
Key Activities
- Challenging Two-Sector Economic Problem: Explored economic models related to demand shock in a two-sector economy, utilizing the Leontief inverse.
- System of Equations for Energy and Manufacturing Sectors: Analyzed the impact of increased demand in the Manufacturing sector on both Energy and Manufacturing outputs.
- LaTeX Formatted Answer Options: Created LaTeX formatted multiple-choice answers for educational purposes.
- Extract Encrypted PDF Link: Developed a Python function using BeautifulSoup for web scraping to extract PDF links.
- Appointment Summary: Summarized the appointment of a new director in Buenos Aires, focusing on legal and publication details.
- Knowledge Graph Construction: Outlined a workflow for building knowledge graphs from natural language summaries.
- Entity and Relationship Extraction Function: Developed a Python function using spaCy and networkx for extracting entities and relationships.
- Improving Entity and Relation Extraction: Enhanced Python code for better parsing and relation extraction in legal texts.
- Relation Extraction Improvements: Analyzed and improved relation extraction functions, focusing on complex sentence structures.
Achievements
- Successfully developed and improved multiple NLP functions for entity and relation extraction.
- Enhanced the parsing and extraction capabilities for legal texts and complex sentences.
Pending Tasks
- Further testing and validation of the improved NLP functions in diverse text domains.
- Integration of the developed functions into broader NLP workflows.