In this project, I led the development and execution of a Named Entity Recognition (NER) pipeline with a strong focus on accuracy, consistency, and quality control. Given the complexity of annotating PERSON, LOCATION, ORGANISATION, EVENT, and MISC entities, I implemented automation strategies to eliminate common annotation errors and streamline the workflow.

Key Automation & Quality Measures Implemented:

Schema Enforcement:
Locked entity labels to prevent custom or inconsistent labels.
Integrated real-time validation to ensure proper annotation format (BILOU/BIO).

Pre-Annotation & AI Suggestions:
Used ML-powered pre-annotations to highlight candidate entities, reducing human oversight.
Applied rule-based suggestions for dates, currencies, and common named entities.

Consistency & Conflict Checks:
Automated detection of inconsistent labeling across documents.
Flagged mismatches in casing, overlapping entities, and repeated unannotated spans.

Automated QA / QC Scripts:
Validated mandatory entity presence and span accuracy.
Detected irrelevant or extra entities, entity leakage (punctuation inclusion), and mislabeling.

Annotator Behavior Monitoring:
Tracked annotation speed and conflict rates, auto-routing high-risk annotations for senior review.
Provided contextual guideline reminders via hover-text in the annotation tool.

UI & Experience Enhancements:
Token-based selection to avoid span errors.
Color-coded entities and in-line guidance for immediate reference.
Snap-to-token feature and disabled freehand text to reduce human errors.

Audit Trail & Version Control:
Maintained a full history of annotations, edits, and reviewer approvals.
Ensured reproducibility and easy tracking of dataset evolution.

Impact:
Reduced annotation errors by 50–70% through AI-assisted pre-labeling and automated checks.
Improved inter-annotator consistency, ensuring high-quality training data for NER models.
Enhanced productivity by minimizing manual rework and accelerating QC processes.