NLP & DOCUMENT AI

Text & Document Annotation Services

Named entity recognition, relation extraction, document parsing, text classification, and OCR correction — with domain expertise across medical, legal, financial, and technical documents in 30+ languages. Every annotation meets measurable F1 and IAA benchmarks.

Discuss Your NLP Needs Text Annotation Tool

2M+

Documents Processed

F1 ≥ 0.93

Entity-Level Accuracy

30+

Languages Supported

κ ≥ 0.85

Inter-Annotator Agreement

15+

Document Types

L1→L2→L3

QA Pipeline

Capabilities

Six Core NLP Annotation Services

Named Entity Recognition (NER)

Precision entity extraction across unlimited custom types — persons, organizations, medical terms, financial instruments, legal clauses, product attributes, and domain-specific terminology. Support for nested entities, overlapping spans, discontinuous entities, and cross-sentence coreference chains.

Relation Extraction & Knowledge Graphs

Annotate directed relationships between entities — drug-disease interactions, cause-effect chains, organizational hierarchies, contractual obligations, and scientific claims. Build the structured knowledge graphs that power question answering, recommendation engines, and decision support systems.

OCR Correction & Document Parsing

Post-OCR quality assurance, field extraction validation, and layout-aware document parsing for scanned forms, invoices, receipts, contracts, handwritten documents, and historical archives. We correct OCR errors, validate field extractions, and annotate document structure for training document AI models.

Text Classification & Sentiment

Document-level, paragraph-level, and sentence-level classification across sentiment polarity, topic categorization, intent detection, urgency scoring, and custom taxonomies. Support for hierarchical labels with configurable confidence thresholds and inter-annotator agreement enforcement.

Document Understanding & Key-Value Extraction

Key-value pair extraction, form field mapping, table structure annotation, section classification, and relationship mapping across PDFs, scanned images, and structured formats. We annotate the spatial and semantic structure that document AI models need to understand complex real-world documents.

Multilingual & Cross-Lingual Annotation

NER, classification, sentiment, and document parsing across 30+ languages — each with native-speaker annotators who understand linguistic nuances, cultural context, and domain-specific terminology in their language. Cross-lingual alignment annotation for multilingual model training.

Medical & Clinical NLP

Clinical note parsing, medical NER (drugs, symptoms, procedures, diagnoses), ICD-10/CPT code mapping, clinical trial document annotation, and HIPAA-compliant de-identification of protected health information.

Legal & Compliance NLP

Contract clause extraction, legal entity recognition, obligation/risk identification, regulatory document parsing, case citation linking, and confidentiality-aware annotation with NDA coverage.

Financial Services NLP

Financial entity extraction, earnings call analysis, SEC/EDGAR filing parsing, KYC document processing, transaction classification, and sentiment analysis on financial news and analyst reports.

E-Commerce & Retail NLP

Product attribute extraction from listings, review sentiment analysis (aspect-level), catalog classification with SKU mapping, search query intent detection, and customer support ticket routing.

Technical Documentation

API documentation parsing, code comment extraction, technical spec annotation, knowledge base structuring, and developer documentation classification for AI-powered developer tools.

Content Moderation & Safety

Toxicity detection, hate speech classification, misinformation labeling, policy violation flagging, and age-appropriateness rating across user-generated content in 20+ languages.

Span Boundary Validation

Automated checks ensure entity spans are clean — no trailing whitespace, partial words, inconsistent boundary definitions, or invalid character offsets. Rule-based validation catches 80% of span errors before human review.

Taxonomy Consistency Enforcement

Entity types, relation labels, and classification categories are validated against the project taxonomy on every annotation. Out-of-schema labels are blocked automatically. Schema versioning tracks changes across guideline iterations.

Cross-Document Entity Consistency

Same entities are labeled the same way across all documents — 'JPMorgan Chase', 'JP Morgan', and 'JPMC' all resolve to the same canonical entity. We track entity-level consistency scores across annotators and batches.

Inter-Annotator Agreement (IAA)

Token-level agreement scores using exact-match F1, Cohen's κ, and Krippendorff's α. Computed per entity type and relation type. Disagreements trigger L3 adjudication and targeted recalibration.

Domain Vocabulary Validation

Custom dictionaries for medical, legal, financial, and technical terminology. Annotators are tested on domain-specific gold sets. Vocabulary updates are distributed to all annotators within 24 hours of approval.

Automated Linguistic Checks

Rule-based validation for language-specific issues: tokenization boundary errors (CJK), script consistency (mixed scripts flagged), encoding issues (UTF-8 validation), and sentence boundary detection accuracy.

Comparison

UTL NLP Annotation vs. Typical Providers

Capability	UTL Data Engine	Typical Provider
Nested & overlapping entity support	✓	Flat entities only
Cross-document coreference resolution	✓
Relation extraction for knowledge graphs	✓
30+ languages with native-speaker annotators	✓	10–15 languages
Domain taxonomy integration (ICD-10, SNOMED)	✓	Generic schemas
Per-entity-type IAA tracking (κ)	✓	Aggregate only
Automated span boundary validation	✓	Manual QA only
Cross-lingual entity alignment	✓
Layout-aware document parsing	✓	Text-only
HIPAA-compliant de-identification	✓	Basic redaction

““We needed custom NER across 50+ medical entity types with nested span support and cross-document coreference. UTL's team understood the clinical domain from day one, delivered 98.5% F1 on our validation set, and maintained κ ≥ 0.87 across 15 annotators. Their automated span validation alone saved us 30% in review time.””

Data Science Director

Enterprise Health-Tech Platform

FAQs

NLP & Document AI Questions

Can you handle nested and overlapping entities?

Yes. We support complex NER schemas with nested entities (e.g., 'Bank of America' tagged as both ORG and LOC), overlapping spans, discontinuous entities, and cross-sentence coreference chains. Our annotation tooling and QA validation are built specifically for these complex span types.

How do you handle domain-specific terminology?

We train annotators on your domain's specific taxonomy, terminology, and edge cases. This includes 20–40 hour domain training, gold set calibration with domain-specific examples, and ongoing terminology dictionary updates. For medical NLP, we integrate ICD-10, SNOMED CT, and MeSH coding standards directly into the annotation workflow.

What about handwritten and degraded documents?

We provide OCR correction and validation for handwritten documents, including historical archives and degraded scans. Our annotators correct OCR errors at character level, validate field extractions, and annotate document structure. For severely degraded documents, we apply multi-pass review with confidence scoring.

Can you build knowledge graphs from annotated data?

Yes. Our relation extraction annotation produces subject-predicate-object triples with evidence spans, confidence scores, and cross-document linking. Output formats include RDF triples, Neo4j import format, and custom graph schemas. We've built knowledge graphs with 100K+ entities and 500K+ relations for enterprise clients.

How do you maintain quality across 30+ languages?

Each language has native-speaker annotators with linguistic training, separate gold sets and calibration processes, and language-specific validation rules (CJK tokenization, Arabic RTL handling, etc.). We measure IAA independently per language and never mix language teams.

What's the typical engagement process?

Scoping + schema design: 3–5 days. Annotator training + calibration: 5–7 days. Pilot (1K–5K documents): 5–10 days. First delivered batch by Day 20. For domain-specific projects (medical, legal), add 3–5 days for domain training. We maintain pre-qualified teams across major domains for faster ramp-up.

Need Text & Document Annotation?

Let's discuss your computer vision data pipeline — from task design to quality-assured delivery. We'll scope a pilot within 48 hours.

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