TextSimAnalyser: Multi-Stage Text Similarity Analysis Toolkit

Intelligent Hybrid Approach for Efficient Text Comparison
Combining Traditional Algorithms with Deep Learning for Optimal Performance

Key Features

• Hybrid Computation Architecture
  Jaccard → Cosine → BERT staged analysis (saves GPU time vs pure-BERT approach)
• Multi-Language Support
  Chinese/English/Korean (paraphrase-multilingual-MiniLM-L12-v2 model)
• Production-Ready
  Docker/Kubernetes support • REST API • Batch processing • LRU caching
• Advanced Visualization
  Interactive heatmaps • PDF/Excel reports • Threshold analysis charts

Application Scenarios

Domain	Use Case	Benefit
Academic	Paper plagiarism detection	Accuracy in duplicate text
Customer Service	FAQ matching optimization	Faster response
Legal	Contract clause comparison	Reduction in manual review
E-commerce	Product description similarity	Improved recommendation relevancy

Usage Case

Basic Analysis

python from textsimanalyser import TextAnalyzer

analyzer = TextAnalyzer(mode="fast") # fast/standard/deep result = analyzer.compare( "Machine learning algorithms", "Deep neural networks" ) print(f"Composite Score: {result['composite_score']:.2%}")

Batch Processing

python batch_results = analyzer.batch_analyze( texts=["Text 1", "Text 2", "Text 3"], output_format="excel", # json/csv/html visualization=True )