Natural Language Processing (NLP) Focus
Project Description Project Scope & Industry: I participated in a large-scale Natural Language Processing (NLP) initiative aimed at optimizing conversational AI for the Banking and SaaS sectors. The project's goal was to improve automated customer service journeys by training models to better understand complex user queries in chatbot logs and email correspondence. Specific Labeling Tasks: Intent Classification: Categorized over 10,000 user inquiries to distinguish between tasks like "Account Recovery," "Billing Disputes," and "Technical Troubleshooting." Named Entity Recognition (NER): Identified and labeled specific data entities such as Transaction IDs, dates, and product names within unstructured text. Sentiment Analysis: Evaluated call-to-text transcripts to tag emotional markers (e.g., frustration, satisfaction) to help the AI determine when to escalate a ticket to a live human agent. Project Size: The project involved processing a dataset of approximately 12,000+ individual text entries over a 4-month period, contributing to the development of a model handling high-volume daily traffic. Quality Measures Adhered To: Data Privacy: Strictly followed PII (Personally Identifiable Information) masking protocols to ensure all financial and personal data remained secure and compliant with banking regulations. Accuracy Standards: Maintained a consistent 98% accuracy rate, verified through a "Gold Standard" comparison and weekly peer-review audits. Edge Case Resolution: Actively participated in feedback loops to refine labeling guidelines when encountering ambiguous or slang-heavy customer language