Building and transforming digital.
The Acronym Classifier is an advanced tool designed to automatically detect and extract acronyms along with their corresponding expansions from large documents. This project combines a user-friendly front-end interface with a powerful back-end processing system, leveraging both custom algorithms and pre-trained NLP models to ensure comprehensive acronym detection and expansion.
Our Acronym Classifier does document analysis by automatically extracting and expanding acronyms, significantly enhancing readability and understanding of technical, legal, and formal texts.
Key Features:
The Acronym Classifier project addresses the challenge of identifying and expanding acronyms in various document types, focusing on both general technical texts and specialized legal documents. The system employs a dual-approach strategy, utilizing different methods to ensure comprehensive acronym detection and expansion.
For general documents, a custom acronym extraction algorithm is implemented. This method uses regex patterns to identify potential acronym-expansion pairs and employs fuzzy matching techniques to verify and refine the detected pairs. The custom algorithm is particularly effective for handling a wide range of document types and writing styles.
Legal and formal documents present unique challenges due to their specialized terminology. To address this, the Blackstone NLP model, specifically designed for legal text processing, is integrated into the system. The model's AbbreviationDetector pipeline is applied to identify abbreviations (acronyms) and their corresponding long forms (expansions) in legal contexts.
The project incorporates advanced text processing techniques, including the use of textract for consistent text extraction from various file formats. After acronym detection, a Named Entity Recognition (NER) model is applied to classify the content of extracted expansions, providing semantic understanding of the acronyms' context.
The final output of the system combines results from both the custom method and Blackstone NLP. A sophisticated conflict resolution mechanism, using fuzzy matching scores and stopword filtering, ensures the selection of the most accurate expansions when methods disagree. The results are presented in a searchable, sortable table on the user-friendly front-end interface and can be exported in JSON format for integration with other systems or further analysis.
This comprehensive approach ensures accurate handling of acronyms across various document types and domains, significantly enhancing document comprehension and analysis capabilities.
