Compare Two Texts Instantly
Paste two passages below to measure lexical overlap and semantic closeness based on shared vocabulary.
Tip: For best results, compare passages of similar length and topic.
What is lexical similarity?
Lexical similarity measures how much two pieces of text overlap in word usage. In plain terms, it asks: how many words (or stems of words) are shared between two passages? This is useful in writing analysis, plagiarism checks, SEO content audits, classroom assessment, and NLP prototyping.
How this calculator works
This tool tokenizes each text, normalizes terms (lowercasing and punctuation cleanup), and then computes multiple similarity metrics. Instead of relying on a single number, it provides a compact panel of scores so you can judge overlap from multiple angles.
Metrics included
- Jaccard Similarity: intersection of unique terms divided by union of unique terms.
- Cosine Similarity: compares term-frequency vectors and captures distribution similarity.
- Dice Coefficient: emphasizes overlap by doubling intersection size.
- Overlap Coefficient: intersection divided by the smaller vocabulary size.
- Composite Score: average of Jaccard, Cosine, and Dice for a single headline number.
When to use lexical similarity analysis
1) Content quality and SEO
Compare two articles targeting the same keyword cluster. If similarity is too high, one page may cannibalize the other. If it is too low, your rewrite might be drifting away from the intended topic.
2) Draft comparison and editing
Writers and editors can compare versions of a draft to quantify how much language changed after revisions. This is especially helpful for collaborative writing teams.
3) Education and assessment
Teachers can compare student responses against reference material or against prior submissions to identify suspiciously close lexical overlap.
How to interpret your score
- 0%–25%: low overlap, likely different topics or vocabulary.
- 26%–50%: moderate overlap, related ideas with different wording.
- 51%–75%: high overlap, likely discussing the same subject with similar terms.
- 76%–100%: very high overlap, often near-rewrite or heavily shared source language.
Always pair numeric scores with human review. Two texts may be semantically similar but lexically different (e.g., heavy synonym use), and vice versa.
Best practices for better results
- Use the stop-word filter to reduce noise from common words (the, and, of).
- Turn on stemming when comparing text variants like “analyze,” “analyzing,” and “analysis-like” forms.
- Avoid comparing extremely short strings; small samples can produce unstable percentages.
- Compare similar genres (blog vs. blog, abstract vs. abstract) for more meaningful interpretations.
Limitations to keep in mind
Lexical similarity does not fully capture meaning. It works at the word level, not deep semantics. For richer analysis, combine this tool with semantic embeddings, topic modeling, or transformer-based similarity methods.
Quick FAQ
Does this detect plagiarism?
It helps flag overlap, but it is not a legal plagiarism detector by itself. Use it as a screening aid.
Can I compare long documents?
Yes. For very large files, compare key sections (introduction, body, conclusion) to get targeted insights.
Why are my scores low even when ideas are similar?
Because lexical methods focus on exact or near-exact words. Strong paraphrasing can reduce lexical overlap while preserving meaning.