NLP Pipeline for Quotation Extraction
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Project Overview
This project involved designing and implementing an advanced natural language processing (NLP) pipeline for quotation extraction and entity classification from unstructured text documents. The system was capable of identifying direct and indirect quotes, attributing them to speakers, and classifying entities within a document corpus.
Challenges Addressed
- Processing diverse text formats with varying quotation styles
- Accurate speaker attribution for ambiguous quotes
- Entity recognition and classification in complex contexts
- Handling large-scale document collections efficiently
Implementation Details
The NLP pipeline consisted of several integrated components:
- Text preprocessing - Cleaning, normalization, and document segmentation
- Quote detection - Pattern-based and learning-based approaches for identifying direct and indirect quotes
- Speaker attribution - Named entity recognition with coreference resolution to link quotes to speakers
- Entity classification - Fine-tuned BERT models to categorize entities and their relationships
- Post-processing - Confidence scoring and contextual validation of extracted information
Results & Impact
The system achieved impressive performance metrics:
- 87% accuracy on complex news article datasets
- Successfully processed over 10,000 documents per day
- Reduced manual processing time by 75%
- Enabled new insights through structured representation of previously unstructured text data
The pipeline has been deployed in production environments for media monitoring, research analysis, and content aggregation.
Technologies Used
- NLP Tools: spaCy, Stanford CoreNLP, Hugging Face Transformers
- Machine Learning: BERT, Fine-tuning, CRF models
- Data Processing: Python, Pandas, Regular Expressions
- System Architecture: Modular pipeline with REST API integration