Apache OpenNLP
Apache OpenNLP is a machine learning based toolkit for the processing of natural language text. It supports common NLP tasks such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution.
Real signal across most facets with visible, nameable gaps — the contract exists but is thin, or the portal is good while governance and commercial terms are absent.
API Evangelist profiles Apache OpenNLP the way a machine reads it — 93 machine-readable artifacts across 10 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Apache OpenNLP scores 51.8/100 (developing), with a separate agent-readiness read of 39/100 (agent aware). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Apache OpenNLP
Each block below is one kind of artifact we hold for Apache OpenNLP. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 10
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Apache OpenNLP Chunking API
The Chunking API from Apache OpenNLP — 1 operation(s) for chunking.
Apache OpenNLP Document Categorization API
The Document Categorization API from Apache OpenNLP — 1 operation(s) for document categorization.
Apache OpenNLP Language Detection API
The Language Detection API from Apache OpenNLP — 1 operation(s) for language detection.
Apache OpenNLP Lemmatization API
The Lemmatization API from Apache OpenNLP — 1 operation(s) for lemmatization.
Apache OpenNLP Models API
The Models API from Apache OpenNLP — 2 operation(s) for models.
Apache OpenNLP Named Entity Recognition API
The Named Entity Recognition API from Apache OpenNLP — 1 operation(s) for named entity recognition.
Apache OpenNLP Parsing API
The Parsing API from Apache OpenNLP — 1 operation(s) for parsing.
Apache OpenNLP POS Tagging API
The POS Tagging API from Apache OpenNLP — 1 operation(s) for pos tagging.
Apache OpenNLP Sentence Detection API
The Sentence Detection API from Apache OpenNLP — 1 operation(s) for sentence detection.
Apache OpenNLP Tokenization API
The Tokenization API from Apache OpenNLP — 1 operation(s) for tokenization.
Scroll within the panel for all 10 ·
Pricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Apache Opennlp Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Apache Opennlp Finops
FINOPSFeatures 10
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
Language Detection
Detects document language using ISO-639-3 classification
Sentence Detection
Splits text into individual sentences with character offsets
Tokenization
Segments text into words and punctuation with position tracking
Named Entity Recognition
Detects persons, locations, organizations, and other named entities
POS Tagging
Assigns Penn Treebank POS tags to each token
Lemmatization
Reduces tokens to their dictionary base forms
Chunking
Identifies noun phrases, verb phrases, and other syntactic chunks
Parsing
Builds full syntactic parse trees using constituency parsing
Document Categorization
Classifies documents into predefined categories
Custom Model Training
Train custom models with Maxent, Perceptron, or Naive Bayes algorithms
Scroll within the panel for all 10 ·
Semantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Apache Opennlp Context
JSON-LDSpectral Rules 2
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Apache OpenNLP API Rules
SPECTRALApache OpenNLP API Rules
SPECTRALJSON Schema 18
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
CategorizationResult
JSON SCHEMAChunk
JSON SCHEMAChunkingResult
JSON SCHEMALanguageDetectionResult
JSON SCHEMALanguageProbability
JSON SCHEMALemmatizationResult
JSON SCHEMAModelInfo
JSON SCHEMAModelList
JSON SCHEMANamedEntity
JSON SCHEMANERResult
JSON SCHEMAParseResult
JSON SCHEMAPOSTaggingResult
JSON SCHEMAPOSTokensRequest
JSON SCHEMASentenceDetectionResult
JSON SCHEMASpan
JSON SCHEMATextRequest
JSON SCHEMATokenizationResult
JSON SCHEMATokensRequest
JSON SCHEMAScroll within the panel for all 18 ·
JSON Structure 18
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Apache Opennlp Categorization Result Structure
JSON STRUCTUREApache Opennlp Chunk Structure
JSON STRUCTUREApache Opennlp Chunking Result Structure
JSON STRUCTUREApache Opennlp Language Detection Result Structure
JSON STRUCTUREApache Opennlp Language Probability Structure
JSON STRUCTUREApache Opennlp Lemmatization Result Structure
JSON STRUCTUREApache Opennlp Model Info Structure
JSON STRUCTUREApache Opennlp Model List Structure
JSON STRUCTUREApache Opennlp Named Entity Structure
JSON STRUCTUREApache Opennlp Ner Result Structure
JSON STRUCTUREApache Opennlp Parse Result Structure
JSON STRUCTUREApache Opennlp Pos Tagging Result Structure
JSON STRUCTUREApache Opennlp Pos Tokens Request Structure
JSON STRUCTUREApache Opennlp Sentence Detection Result Structure
JSON STRUCTUREApache Opennlp Span Structure
JSON STRUCTUREApache Opennlp Text Request Structure
JSON STRUCTUREApache Opennlp Tokenization Result Structure
JSON STRUCTUREApache Opennlp Tokens Request Structure
JSON STRUCTUREScroll within the panel for all 18 ·
Examples 18
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Apache Opennlp Chunk Example
EXAMPLEApache Opennlp Span Example
EXAMPLEScroll within the panel for all 18 ·
Security Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Use Cases 5
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
Information Extraction
Extract structured data from unstructured text documents
Text Classification
Automatically categorize documents by topic or sentiment
Search Enhancement
Improve search relevance with NLP-based query processing
Content Analysis
Analyze large text corpora for entities, topics, and patterns
Chatbot Development
Build conversational AI with NLP intent and entity extraction
Integrations 5
Pre-built integrations with other platforms tell you where this provider already fits in a stack.
Pre-built integrations with other platforms and tools.
Apache Solr
Integrate OpenNLP with Apache Solr for NLP-enhanced search
Apache Lucene
Use OpenNLP analyzers in Lucene text processing pipelines
Apache Flink
Real-time NLP processing with Apache Flink data streams
UIMA
Apache UIMA framework integration for unstructured information analysis
Maven/Gradle
Available on Maven Central for Java build system integration
Resources
Every other property we hold for Apache OpenNLP — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 1
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 3
Pagination, idempotency, versioning, errors, and events
Build 1
SDKs, sample code, and the tooling you integrate with
Access & Security 2
Authentication, authorization, and security posture
Company 1
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/apache-opennlp · machine-readable index on apis.io