Text Lemmatizer API

About the Text Lemmatizer API

A text lemmatizer API that reduces words to their dictionary base form. Lemmatizer looks up each word in the text and returns its dictionary base form: plurals become singular (geese to goose), verbs become their base form (went to go, running to run) and comparatives their root (better to good). Common short words such as "the" and "of" are skipped.

Lemmatization is a standard text normalization step in NLP work. Grouping "run", "runs" and "running" under one lemma gives cleaner keyword counts, better search matching and tidier features for classifiers and topic models than raw word counts, and unlike crude suffix stemming it keeps real words, so "fences" becomes "fence" rather than "fenc".

The response counts how many distinct lemmas were found. On the Free plan you receive only that count. Paid plans also receive the lemmas themselves, each mapped to how many times it occurs in the text, which is what you need to build a word-frequency table or index documents by root form.

How it works

Lemmatizer parses text with the compromise natural-language library and asks it to compute a root for every term. compromise tags each word with its part of speech and uses that to find the dictionary form, which is why irregular forms such as "geese" and "went" resolve to "goose" and "go". Before parsing, the first tab, carriage return and line feed in the text are replaced with spaces and the text is trimmed.

For each term the normalized word is checked against a short stopword list (the, a, an, in, on, at, to, from, for, with, by, of), and those words are skipped. The root has any trailing possessive "'s" removed. Because compromise maps forms of "be", "have" and "do" inconsistently, the service applies its own table on top: is, am, are, was, were, been and being become be; has, had and having become have; does, did and done become do. Each resulting lemma is counted, and different surface forms of the same word add to the same count.

lemmas is an object keyed by lemma with an integer occurrence count as the value, and found is the number of keys. The object is capped at the first 50 distinct lemmas in the order they first appear; later ones are dropped and found reports the capped total. Words that are already in base form are included, and words compromise cannot resolve fall back to their normalized spelling, so some past-tense forms can appear unchanged. If no lemma is produced, the request returns a 404. On the Free plan lemmas is null and only found is returned.

What people use it for

Search Query Normalization

Search engines convert user queries into root words to match plural or conjugated variations against indexed product titles.

Customer Feedback Clustering

Group incoming support tickets by topic after reducing verbs and nouns to base forms for cleaner text classification.

Vocabulary Learning Systems

Language learning platforms detect inflected verb forms across reading exercises and map them back to headwords in student flashcards.

SEO Content Auditing

Before scanning articles for keyword density, content marketing platforms strip grammatical suffixes to measure true term frequencies across drafts.

Ways to call it

One endpoint, many ways in — REST with JSON, XML, YAML and CSV, plus GraphQL and an MCP interface for AI agents.

JSON

Default REST response

XML

Markup format

YAML

Human-readable

CSV

Tabular export

Beta

GraphQL

Query language

New

MCP

For AI agents

Request parameters — POST /v1/lemmatizer

Sent as JSON in the request body. Premium parameters are accepted on every plan but only take effect on plans that include them. Anything not listed here is dropped rather than passed through.

Required

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curl -X POST "https://api.apiverve.com/v1/lemmatizer" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"text":"The cats ran from door to door"}'

Authentication

Send your key in the x-api-key header. That is the only auth step — no token exchange, and no per-endpoint scope to configure.

Response — POST /v1/lemmatizer

Every APIVerve endpoint returns the same three top-level keys, so one response handler covers your whole integration: status, error and data. Only data changes shape.

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{
  "status": "ok",
  "error": null,
  "data": {
    "found": 3,
    "lemmas": {
      "cat": 1,
      "ran": 1,
      "door": 2
    }
  }
}

Response fields

Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.

Response headers

Every response carries your balance, so your own code always knows where it stands without polling anything. All four are exposed to browsers through CORS.

Errors

Read the HTTP status first, then error for the specific reason. The body names the parameter that has to change.

What a call costs — 2 credits per call

Credits are shared across every APIVerve API — one balance, one key, one bill. Failed requests never count against it, so a rejected input costs nothing.

Calls are what the credits buy at this API's rate — spend them here, on any of the other APIs, or across both. Nothing is reserved per endpoint.

Zero-code embed — one snippet, no backend

Drop an interactive Text Lemmatizer form onto any page. The widget calls the API for you, so no key ever appears in your markup and there is nothing to deploy.

Changelog — Text Lemmatizer API

Every change to this endpoint, newest first. Breaking changes ship as a new version and the previous one keeps serving — response fields are added, never removed or retyped in place, so an integration written against v1 keeps working.

Currently v1