Count top NFC and case-normalized word-like tokens with total, unique, deterministic tie, displayed-row, multilingual segmentation, and semantic limitations.
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A labeled set of measurements derived from that text
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Count and rank the top displayed NFC/case-normalized word-like tokens in the exact entered text with explicit segmentation, tie, truncation and semantic limits.
See repeated surface tokens without a hidden stop-word or importance claim.
Enter the exact draft, inspect total/unique tokens and top rows, then review repetitions in context.
Can revise or retain wording without interpreting frequency as quality or engagement.
Preserve accented and non-Latin letters and know how word-like boundaries were derived.
Test case, accents, combining marks, CJK and mixed scripts; inspect NFC normalization and runtime segmentation disclosure.
Can reproduce displayed rows and identify linguistic limitations.
Prevent frequency output from becoming a sentiment, originality, keyword, bias or clarity verdict.
Compare the rows with the claim/source matrix and affected-person review rather than optimizing automatically.
Can reject unsupported interpretation while retaining the descriptive record.
Outputs and checklists are planning aids. Review the linked current authorities and the records, terms, instructions, and requirements that apply to your exact situation before a consequential decision.
This tool is part of these guided projects. Each project provides step-by-step instructions with checklists and all the tools you need in one place.
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Intl.Segmenter supplies locale-sensitive word-like candidates when available; a documented CJK/whitespace fallback is used otherwise.
Tokens are normalized to NFC and locale-lowercased, stripped only of non-letter/number/mark edges, counted, sorted by descending count and then by token for ties, and truncated to 10 display rows.
Frequency is not importance, keyword quality, originality, sentiment, bias, clarity, truth, accessibility or causal performance evidence.
Updated: August 2026
Inspect repeated surface forms in context instead of automatically deleting the most frequent term.
Review token boundaries and fallback status before comparing counts across languages or versions.
Compare repeated terms with the source and claim matrix without treating frequency as a truth, quality, originality or performance score.
The panel shows the top 10 normalized surface tokens from the exact text. It preserves Unicode letters, numbers and combining marks, but it does not stem, lemmatize, merge synonyms or infer meaning.
