English Lemmatizer & Porter Stemmer

The English Lemmatizer & Porter Stemmer is a free online tool that reduce english words to their base forms with the porter stemmer algorithm or a rule-based lemmatizer. compare both side by side.. It runs entirely in your browser with no installation or sign-up needed.

Stemming chops off word endings heuristically (running->run, studies->studi) — stems may not be real words. Lemmatization returns the dictionary base form (studies->study, went->go) — lemmas always are.

Classic Porter stemmer (Porter 1980) implemented in your browser — all 5 steps including m>0/m>1 measure conditions, *v*, *o, and *d tests.

No. All stemming, lemmatization, and export happen entirely in your browser. Your text never leaves your device.

Yes, the English Lemmatizer & Porter Stemmer is 100% free with no registration, no hidden fees, and no usage limits. All processing happens locally in your browser, ensuring complete privacy.

Absolutely. The English Lemmatizer & Porter Stemmer processes everything client-side in your browser. No data is uploaded to or stored on any server. Your content remains private on your device at all times.

Yes, the English Lemmatizer & Porter Stemmer is fully responsive and works on smartphones and tablets. You can use it on any device with a modern web browser -- no app download required.

No account or registration is needed. Simply open the English Lemmatizer & Porter Stemmer in your browser and start using it immediately. There are no sign-up walls or usage restrictions.

Yes, the English Lemmatizer & Porter Stemmer handles text of any length with fast, real-time processing. Since everything runs in your browser, performance depends on your device but works well for most use cases.

Simply enter your input in the provided field, adjust any settings to your preference, and the tool will process it instantly. You can then copy the result to your clipboard or download it.

Reach for stemming when speed and aggressive vocabulary collapse matter more than producing real words. Stemming uses simple suffix-chopping rules, so it is fast and consistent — ideal for search indexes, bag-of-words models, and information retrieval where "studies", "studied", and "studying" only need to map to one shared token, even if that token is "studi". Lemmatization is the better choice when the output must be human-readable or grammatically valid, such as displaying normalized terms, building a clean keyword list, or feeding a dictionary-based pipeline, because every lemma is a genuine word. Many NLP workflows use stemming for matching and lemmatization for presentation. This tool runs both at once on the Compare tab and reports a separate reduction percentage for each, so you can see which technique fits your text before committing.

The Porter stemmer is a rule-based algorithm, not a dictionary lookup, so it transforms endings by pattern rather than checking whether the result is a real word. For "studies" it applies the rule that changes a final "-ies" to "-i", producing "studi". That stem is intentional: every word sharing the root collapses to the same token, which is all a search index or text-matching model needs. A real word is never guaranteed — "argument" and "argue" can even stem differently. If you need the valid dictionary form "study", use lemmatization instead, which maps "studies" to "study" using base-form rules. Switch to the Lemmatizer tab here to get readable lemmas, or use Compare to view the stem and lemma for every word side by side and spot exactly where they diverge.

The Remove stop words toggle drops very common function words — like "the", "of", "and", "is", and "to" — before the tool reduces the remaining words to stems or lemmas. These words appear in almost every sentence but carry little topical meaning, so filtering them out leaves you with the content-bearing terms that actually distinguish one passage from another. Enable it when you are building a search index, extracting keywords, counting distinct ideas, or preparing text for a bag-of-words model, because it shrinks the vocabulary and reduces noise. Leave it off when you need a faithful, word-for-word reduction of the original text, such as linguistic analysis where every token matters. Combine it with the Lowercase first and Keep numbers options to fine-tune exactly which tokens survive, then check the statistics panel to see how many words remained.

The reduction percentage measures how much smaller your vocabulary becomes after every word is reduced to its root. It compares the number of unique original words with the number of unique stems or lemmas: if 200 distinct words collapse to 150 distinct stems, that is a 25% reduction. A higher number means your text contains many inflected variations of the same roots — plurals, verb tenses, and comparatives — so it carries fewer truly distinct ideas than the raw word count suggests. This tool reports a separate reduction figure for stemming and for lemmatization, since the more aggressive stemmer usually collapses more. The metric is useful for gauging lexical richness, sizing a search index, or checking whether writing leans on repeated concepts. Paste a passage and watch the statistics panel update the reduction live as you adjust the options.

Yes. The lemmatizer pairs suffix rules for regular endings with a built-in dictionary of common irregular forms, so it resolves words that simple rules would miss. Irregular verbs map to their base form — "went" becomes "go" and "bought" becomes "buy" — irregular plurals are handled, with "feet" becoming "foot" and "mice" becoming "mouse", and irregular comparatives and superlatives reduce too, so "better" and "best" both become "good". Regular endings like "-s", "-es", "-ies", "-ed", "-ing", "-er", and "-est" are stripped by rule. Because it is a lightweight, deterministic lemmatizer rather than a full part-of-speech tagger, it works without sentence context, which keeps it fast and predictable. Load the built-in sample sentence to see irregular verbs, plurals, and comparatives all normalized in a single pass before you paste your own text.