Cloud Ibm Natural Language Understanding

Name: Natural Language Understanding API
Brand: Natural Language Understanding
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLMl InferenceapiKey14 EndpointsREST

For Agents

Extract entities, keywords, sentiment, emotion, categories, and relations from text or web pages, and manage custom classification models.

Install Jentic One Beta

Connect the Natural Language Understanding to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Natural Language Understanding, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with Natural Language Understanding API.

Analyse free text, raw HTML, or a public URL for entities, keywords, sentiment, emotion, and concepts

Detect categories and high-level topics in long-form content with the built-in taxonomy

Extract semantic roles, syntax tokens, and relations between entities in a document

GET STARTED

Start building with Natural Language Understanding API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI specification for Natural Language Understanding, keeping it validated and agent-ready. IBM Watson Natural Language Understanding analyses text, raw HTML, or a URL and returns structured features such as entities, keywords, concepts, categories, sentiment, emotion, syntax, semantic roles, and relations. The service strips advertisements and boilerplate from HTML before analysis and supports custom Watson Knowledge Studio models for domain-specific entity and relation extraction. Custom classification and categorisation models can be trained, listed, and deleted directly through the API.

Use Cases

Patterns agents use Natural Language Understanding API for, with concrete tasks.

★ Brand and Entity Monitoring

Pull entities and sentiment out of news articles, blog posts, and social content to track how a brand is mentioned across the web. The /v1/analyze endpoint accepts a URL or raw HTML, removes ads and navigation, and returns extracted entities with confidence scores and per-entity sentiment, which is enough signal to drive a daily brand monitoring report. The same call returns concepts and keywords for thematic analysis.

Call POST /v1/analyze with a URL and features={entities, sentiment, keywords} and return the top 10 entities ranked by relevance

Support Ticket Classification

Train a custom classification model on historical support tickets and route new tickets automatically by predicted label. The /v1/models/classifications endpoints handle the full lifecycle — create, list, retrieve, update, and delete a model — and the analyze endpoint applies the trained model at inference time. This replaces brittle keyword-rule routing with a model that adapts to the way customers actually phrase their issues.

Train a classifications model from a CSV of labelled tickets, then call /v1/analyze with classifications.model={model_id} on a new ticket

Content Categorisation

Map articles or product descriptions onto a customer-defined hierarchy using a custom categorisation model. The /v1/models/categories endpoints expose model lifecycle calls, and analyze applies the model to new content with hierarchical category scores. This is useful for content management systems, ad targeting, and recommendation pipelines that need consistent topic labels.

Submit a labelled training set to POST /v1/models/categories, poll until the model is available, then run /v1/analyze with categories.model on new articles

Voice-of-Customer Analytics

Run customer feedback, reviews, and survey responses through the analyze endpoint to extract emotion, sentiment, and the entities customers discuss most. Per-entity sentiment makes it possible to say 'shipping is rated negatively while product quality is rated positively' rather than reporting a single average score, which is the typical bar for a useful CX dashboard.

POST /v1/analyze with text=review and features={sentiment.targets, emotion, entities} and return per-target sentiment

Agent-Driven Text Analysis via Jentic

AI agents that need text analytics can search Jentic for an intent like 'extract entities from text' and Jentic returns the IBM NLU analyze operation with its full input schema. The IAM API key lives in the Jentic vault, so the agent receives a scoped credential per call and never logs the raw key. This collapses what is normally a multi-step IBM Cloud onboarding into a single search-load-execute pattern.

Search Jentic for 'analyse text for entities and sentiment', load the analyze operation, execute with the user's text, and return entities plus document sentiment

Key Endpoints

14 endpoints — jentic publishes the only available openapi specification for natural language understanding, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/v1/analyze

Analyse text, HTML, or URL for selected features

GET

/v1/models

List Watson Knowledge Studio models

POST

/v1/models/categories

Train a custom categorisation model

GET

/v1/models/categories

List categorisation models

POST

/v1/models/classifications

Train a custom classification model

GET

/v1/models/classifications

List classification models

DELETE

/v1/models/{model_id}

Delete a Watson Knowledge Studio model

POST

/v1/analyze

Analyse text, HTML, or URL for selected features

GET

/v1/models

List Watson Knowledge Studio models

POST

/v1/models/categories

Train a custom categorisation model

GET

/v1/models/categories

List categorisation models

POST

/v1/models/classifications

Train a custom classification model

Why Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

IBM Cloud IAM API keys are stored encrypted in the Jentic vault. Agents receive a per-call scoped token in the Authorization header so the raw IAM key never enters the agent's context or logs.

Intent-based discovery

Agents search Jentic with intents like 'extract entities from text' or 'classify ticket' and Jentic returns the IBM NLU analyze operation with its full input schema, removing the need to read IBM's API reference.

Time to first call

Direct IBM Watson NLU integration: 1-2 days for IBM Cloud account setup, IAM key issuance, and instance provisioning. Through Jentic: under 30 minutes — search, load, execute.

FAQs

Specific to using Natural Language Understanding API through Jentic.

Why is there no official OpenAPI spec for Natural Language Understanding?

IBM Cloud does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Natural Language Understanding via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Natural Language Understanding API use?

The API uses IBM Cloud IAM, which is presented as an apiKey scheme in the Authorization header. Through Jentic, the IAM API key is stored encrypted in the vault and a scoped credential is issued per call so the raw key never enters the agent's context.

Can I extract sentiment per entity with this API?

Yes. POST /v1/analyze accepts a sentiment.targets array or sentiment.document=true and returns sentiment scores both at the document level and per entity or keyword, which lets you separate signals like positive product feedback from negative shipping feedback in the same response.

What are the rate limits for the Natural Language Understanding API?

Rate limits depend on the IBM Cloud plan. Lite plans cap usage at 30,000 NLU items per month and a small number of requests per second, while Standard and Premium plans raise both the throughput and the items-per-month allowance. Check the IBM Cloud plan page for the exact tier limits.

How do I analyse a piece of text for entities and sentiment through Jentic?

Run pip install jentic, search for 'analyse text for entities and sentiment', load the analyze operation, then execute with text and features={entities, sentiment}. The call hits POST /v1/analyze and returns the structured analysis.

Can I train custom models against this API?

Yes. The /v1/models/classifications and /v1/models/categories endpoints support full lifecycle management for custom classifiers and categorisers, and the trained model_id is then passed into /v1/analyze at inference time. Watson Knowledge Studio is the recommended path for custom entity and relation models.

Cloud Ibm Natural Language Understanding

Connect the Natural Language Understanding to your agent

Capabilities

Start building with Natural Language Understanding API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs