Find repeated ASCII word tokens and remove adjacent repeats

The finder counts tokens made from ASCII letters, digits, apostrophes and hyphens. Filter by minimum length, case sensitivity and a fixed English stop-word list. Frequency analysis and consecutive-repeat removal are separate operations; a repeated term can be appropriate and is not proof of plagiarism.

Worked examples

At the default minimum length of 4 with case sensitivity off, constructor Constructor constructor produces one repeated token with count 3. Display casing comes from its last occurrence. Turning case sensitivity on separates differently cased tokens.

For alpha beta alpha the the, the frequency list includes alpha twice under the default settings. Remove Consecutive Repeats changes the text to alpha beta alpha the. The two non-adjacent occurrences of alpha remain.

Understand the limits

Removal is case-insensitive regardless of the frequency switch, and it does not use the stop-word or minimum-length filters. It repeatedly collapses adjacent matching tokens separated by whitespace. Review the result: a phrase such as had had can be grammatically intentional.

This is not a language-aware tokenizer. Non-Latin scripts and accented words are not reliably recognized as whole words; punctuation outside the supported token pattern acts as a separator. Numbers and hyphen sequences can also enter the count if they meet the length filter.

A missing repeated term may be below the chosen length or in the stop-word list. Lower the length or change filters to investigate. The tool does not remove every later occurrence in a document, compare sources or calculate an originality score.

Processing and privacy

Analysis and cleanup run locally without a word-analysis API request. The site still loads assets. Empty or whitespace-only input clears the frequency list. Keep a source copy before applying cleanup, then read the edited sentence rather than relying only on the count.

Use a fictional inspection sentence before a real document

Paste alpha beta alpha the the with the default settings. Alpha should appear twice in the frequency findings, while the short stop word the does not qualify for that list. Select Remove Consecutive Repeats and inspect the source afterward: the adjacent pair the the becomes one occurrence, while the separated alpha terms remain. This exercise separates the two operations visibly. A frequency list describes how often a supported token appears; cleanup changes only a particular adjacency pattern. Keep that distinction when reviewing a document where repeated terminology can be intentional and necessary for clarity.

Test whole-token boundaries rather than matching prefixes

Use cat cat's as a counterexample. The second token is not an identical copy of the first, so cleanup must retain both. Likewise alpha alpha-beta contains two different supported tokens. Identical tokens separated only by whitespace can collapse across a line break, but a comma or other intervening punctuation prevents that adjacency rule from applying. Inspect an actual sentence after cleanup instead of assuming that a lower duplicate count is always better. The operation is case-insensitive and retains the first token's spelling in a collapsed run; it does not rewrite the whole paragraph into one chosen capitalization style.

Inspect the filter settings independently

Frequency analysis uses the minimum-length slider, case-sensitive switch and optional fixed English stop-word list. At the default minimum of four, constructor Constructor constructor has a combined count of three when case sensitivity is off. Enabling case sensitivity separates the capitalized occurrence. Reducing a length threshold can reveal tokens previously excluded from the report, while changing stop-word handling only affects terms in the supplied list. These are search parameters, not corrections to the source. They do not change which adjacent repeats the cleanup action removes, and they do not establish a document's reading level or grammatical correctness.

Keep tokenization limitations visible

The token pattern is intentionally ASCII-oriented. It can split an accented name into pieces and does not interpret non-Latin writing systems as a language-aware word segmenter would. Digits, apostrophes and hyphens can participate in the supported tokens. Consequently a report containing repeated identifiers or a hyphen sequence is not necessarily reporting repeated ordinary English words. Inspect the source around an unexpected token before changing it. Use a language-aware editorial workflow for multilingual text rather than treating a missing frequency finding as evidence that no repetition exists in the document.

Stay within the analysis and display bounds

The editor accepts up to 100,000 UTF-16 text units, and the findings display shows up to five hundred repeated tokens ordered by frequency. These limits keep the interactive report bounded; they do not promise that every possible repeated token in a very diverse document will be displayed at once. Very long token labels are shortened visually, with the complete token available through its hover title. Preserve your source elsewhere before editing or cleaning it. There is no document upload, tracked-change history, automatic undo record, cross-document comparison or originality percentage generated by this page.

Record an editorial decision rather than a score

For a real revision, note the repeated token, its sentence context and the reason you kept or changed it. A product code may need to repeat to avoid ambiguity, while an accidental adjacent word may deserve deletion. Frequency alone cannot distinguish those cases, and absence of reported duplicates cannot establish original authorship. Analysis and cleanup are local operations without a word-analysis API or saved document history. Ordinary site services remain separate under the Privacy Policy. If you use the result in an editorial process, retain the original text and the actual revised sentence so another reviewer can assess what changed.

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