The Cross-Lingual Text Similarity is a Natural Language Processing project that combines Duplicate detection and Batch processing, built with spaCy. The project follows a clean, modular pipeline where text loading, processing, and presentation stay separated, making it easy to test, extend, and present. It showcases practical NLP techniques while producing a working, demo-ready application.
Traditional manual methods for this task are slow, inconsistent, and unable to scale as text data grows. Without an NLP-based approach built on Batch processing and spaCy, users cannot process, classify, or extract meaning from large amounts of text quickly, and there is no reliable way to evaluate the quality of the results.
This project applies NLP techniques through Duplicate detection, orchestrated with spaCy and Batch processing. The pipeline is designed for quality and control, with preprocessing, feature extraction, and clear evaluation. It produces consistent, reusable results and can be adapted to related text tasks with minimal changes.
spaCy
FuzzyWuzzy
Python 3.11+
Pandas and NumPy
NLTK / spaCy
Evaluation and logging
scikit-learn
NLTK
Modular text pipeline around Duplicate detection and Batch processing
Configurable preprocessing and model settings
Clear logging, metrics, and error handling
Clean interface for viewing results
Reusable components for related NLP tasks
Evaluation of output quality
The project is layered: the input layer loads and preprocesses text through Duplicate detection; the processing layer applies NLP models with spaCy and Batch processing; and the output layer formats and presents results via Similarity computation. Shared configuration, logging, and evaluation modules support all layers, keeping the system robust and easy to extend.
Set up the Python environment, project structure, and configuration files.
Build the input and preprocessing layer with Duplicate detection.
Implement the core NLP pipeline using spaCy and Batch processing.
Add the output and presentation layer via Similarity computation.
Wire up end-to-end flows and add error handling and logging.
Evaluate results, tune parameters, and refine the pipeline.
Package the project, document it, and prepare the demo and viva report.
Build production-style NLP applications
Apply Vectorizing documents and Detecting plagiarism and duplicates
Preprocess and analyze real text data
Work with popular NLP libraries
Present and defend a complete NLP project in viva
Expose the pipeline as a REST API for other apps
Add deep learning models for higher accuracy
Add multilingual support
Deploy with caching for faster responses
The Cross-Lingual Text Similarity delivers a complete Natural Language Processing workflow — from text loading and preprocessing to analysis and presentation. It is practical, modern, and easy to explain, making it an excellent final year project that demonstrates in-demand NLP skills.
