📅 2024-09-11 — Session: Developed NLP Techniques for Entity and Relation Extraction
🕒 22:10–23:20
🏷️ Labels: NLP, Entity Extraction, Relation Extraction, Python, Spacy
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal: The session aimed to enhance natural language processing (NLP) techniques for entity and relation extraction, focusing on improving parsing and extraction in complex legal and structured texts.
Key Activities:
- Developed a Python function using spaCy and networkx for entity and relationship extraction, creating graph structures from sentences.
- Improved entity and relation extraction in legal texts by addressing conjunctions, indirect objects, and complex sentence structures.
- Enhanced sentence parsing for better recognition of subjects, verbs, and objects, focusing on actions involving persons and institutions.
Achievements:
- Successfully implemented a structured approach for building a knowledge graph from natural language summaries.
- Improved the accuracy of relation extraction functions by refining parsing techniques and updating Python code.
Pending Tasks:
- Further testing and validation of the improved NLP techniques on diverse datasets to ensure robustness and accuracy.
- Integration of the developed functions into larger NLP workflows for comprehensive text analysis.