NLP-Enhanced Intelligence Analysis (PACTASE)

Diagram showing the data labeling workflow and event classification system developed for the project.

Project Overview

PACTASE was a specialized NLP project developed in collaboration with government scientists to classify adversary tactics within large audio datasets. The project aimed to automate the identification of critical information that traditionally required hours of manual review by analysts.

Subject Matter Expert Role

As the SME lead on this project, I:

• Defined and managed the comprehensive data labeling strategy
• Developed protocols for tagging key events, terminology, and signal characteristics
• Collaborated directly with scientists to iterate on classification approaches
• Created procedures for handling ambiguous data and edge cases

Strategic Pivot & Impact

After recognizing the limitations of automated transcription on noisy, real-world data, I proposed a fundamental shift in our approach:

• Redirected focus from comprehensive transcription to high-value event tagging
• Developed a tiered event classification system based on intelligence value
• Created clear protocols for handling signal ambiguity
• Implemented quality control processes for labeled data

This strategic pivot dramatically improved project outcomes:

• Reduced analyst review time from hours to minutes per audio segment
• Produced a high-fidelity labeled dataset that served as training data for subsequent ML models
• Made the project significantly more achievable and impactful within resource constraints

Cross-Functional Collaboration

The project required extensive collaboration across disciplines:

• Regular coordination with NLP scientists to refine the labeling schema
• Clear communication of technical constraints to operational stakeholders
• Translation of domain expertise into implementable technical requirements
• Iteration based on scientist feedback and preliminary model performance

Skills Demonstrated

This project showcases my abilities in:

• Bridging technical and domain expertise
• Strategic problem-solving and pivoting when faced with technical limitations
• Developing structured approaches to ambiguous data problems
• Effective cross-functional communication
• Balancing theoretical ideals with practical implementation realities

The labeled dataset created through this project continues to serve as a foundation for ongoing machine learning development in audio analysis applications.