Service - Text Intelligence

NLP Annotation

High-accuracy text labeling for named entity recognition, intent classification, sentiment analysis, relation extraction, and more. AI pre-labeling cuts annotation time by 40% human experts validate and correct everything.

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40%
Faster annotation via AI pre-labeling with human expert validation
≥0.72
Cohen's kappa minimum on every NLP annotation batch delivered
12+
Supported languages across domains
6
Core NLP annotation task types with domain-specialist annotators
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Named Entity RecognitionIntent ClassificationSentiment AnalysisRelation ExtractionCoreference ResolutionText ClassificationDependency ParsingSemantic Role LabelingNamed Entity RecognitionIntent ClassificationSentiment AnalysisRelation ExtractionCoreference ResolutionText Classification
NLP Named Entity Recognition
● PERSON ENTITY
● ORGANIZATION
● LOCATION
● DATE / AMOUNT
▼ ENTITY TYPES DETECTED
PERSON
ORG
LOC
DATE
AMOUNT
✓ 38,420 ENTITIES TAGGED
What It Is

Structured text labels that teach machines to understand language

NLP annotation is the process of adding structured labels to raw text so that machine learning models can learn linguistic patterns. Every search engine, chatbot, document processor, and voice assistant relies on millions of carefully annotated text examples to understand what human language means.

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Live Annotation Interface

Named Entity Recognition Labelling Tool

Domain-specialist annotators tag entities across legal, financial, and news corpora building training sets for production NER models.

ConcaveLabel Studio - NER Annotation · Corpus: SEC Enforcement Orders · 14,820 sentences

James Mitchell, former CFO of NovaTech Solutions Inc., was found by SEC to have made undisclosed trades on March 14, 2023 prior to the merger announcement with Atlas Digital Corp. The total gain was estimated at $4.2 million.

The order, issued from the SEC New York Regional Office, imposes a $1.8 million penalty and bars Mitchell from securities markets for 3 years effective 01 April 2024.

Counsel Sarah Chen of Chen & Partners LLP, New York, filed an appeal at the U.S. Court of Appeals citing procedural violations under Regulation 10b-5.

ENTITY TYPES
PERSON
ORGANIZATION
LOCATION
DATE
AMOUNT
REGULATION
Entities Tagged:38,420 IAA Score:0.91 F1 Languages:EN / DE / ES Annotators:12 Legal Specialists
How It Works

Three things the pipeline does on every NLP project

Domain-routed text annotation
Tasks auto-routed to the right specialist pool by domain such as legal, automotive, agriculture, finance, or tech. No generalists on domain-specific corpora. Routing is schema-enforced, not manual assignment.
Span labeling with disagreement adjudication
Every annotation span goes through automated disagreement detection, peer adjudication, and expert sign-off. Disagreement logs exported with every batch so you can audit every adjudicated label decision.
Verifiable inter-annotator agreement
F1 and Cohen's kappa published per label type, per annotator, per batch not averaged across the full corpus. Per-entity-boundary accuracy reported alongside label accuracy.
Pipeline Capabilities

What the infrastructure delivers

Multilingual Routing
Automatic language detection routes each document to the appropriate specialist annotator pool—domain coverage and language coverage managed as a single pipeline, not separate workstreams.
Entity Consistency Enforcement
Named entity and coreference labels are validated for cross-document consistency before delivery. Duplicate entities, boundary drift, and schema violations are caught automatically, not by manual audit.
Intent Hierarchy Annotation
Intent taxonomies are applied at the utterance level with slot-fill labels for NLU training—hierarchical intent structures supported with explicit parent-child validation built into the pipeline.
What You Get

Annotated datasets backed by verifiable quality proof

Annotated Dataset
Labeled data in your preferred format: CoNLL-2003, IOB2, BRAT standoff, JSONL, CSV, or spaCy DocBin. Each document includes annotation spans, label types, annotator IDs, confidence flags, and adjudication decisions for resolved disagreements.
QA Report
Per-annotator kappa and F1 by label type, gold standard accuracy, disagreement log with adjudication decisions, label distribution statistics, and entity boundary consistency analysis. Every metric verifiable against raw annotation logs.
Data Card & Annotation Guide
ML data card with annotator profiles, label definitions, known ambiguities, out-of-scope cases, and quality thresholds applied. The full annotation guideline document is included so future annotators or reviewers can audit any label decision.
Pricing

Per-document
transparent pricing

NLP annotation is priced per document based on task complexity, document length, and domain. Volume discounts apply at 5,000+ documents.

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Basic NER / sentiment (short docs)$1.25–2.50 / doc
Multi-label classification$1.75–3.50 / doc
Complex NER + relations$3–5 / doc
Agriculture/ AV / legal / finance NLP$3.50–7 / doc
Coreference resolution$2.50–5 / doc
Minimum project size500 documents

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Send us 100 text samples from your domain. We will annotate them with our expert team and return a kappa report and label distribution analysis at no cost.

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