Text Matching Tool

The tool computes a similarity score between two input texts using configurable metrics. It targets researchers, editors, QA engineers, educators, and product teams who need quick, repeatable similarity metrics to assess duplicates, paraphrase risk, or content reuse. Use cases include plagiarism checks, version comparison, editorial audits, and data cleaning in content migration. Example: a researcher compares two abstracts to quantify overlap; a writer checks if revised copy remains substantially similar to an original draft.
Core logic and processing: Two text inputs are normalized and tokenized, then transformed into numeric representations according to the selected metric (Cosine with TF-IDF, Jaccard, or Levenshtein). The tool can apply optional stop-word handling and n-gram boosting. Outputs include a similarity score in [0,1], an optional percentage, and optional alignment data showing matched segments.
Algorithms and calculations: Cosine uses TF-IDF vectors; score = dot(v1,v2)/(||v1||*||v2||). Jaccard uses token-set overlap; Levenshtein yields an edit-distance-based similarity. The system may report top matches or a single aggregate score, with thresholding to filter results.
Industry and usage: Designed for UTF-8 text in English and other languages depending on the tokenizer. In multilingual contexts, results depend on tokenization quality. The tool is deterministic per run, scalable to long texts with chunking if needed.
The tool focuses on explicit text similarity metrics and does not perform deep semantic analysis; results should be interpreted as metric-based similarity.