Text Annotation Services for AI & NLP
Power your natural language processing models with precise, multilingual text annotation from Australia's trusted data labeling experts.
Why Text Annotation Accuracy Matters
Your NLP model's performance depends entirely on the quality of your training data. Inconsistent labels, missed entities, and poorly annotated sentiment create models that fail in real-world applications. AI Taggers delivers enterprise-grade text annotation that ensures your language models understand context, intent, and nuance.
Trusted by AI research teams, enterprise NLP developers, and government agencies to annotate millions of text samples across 120+ languages.
Our Text Annotation Capabilities
Named Entity Recognition (NER)
Identify and label people, organizations, locations, dates, products, and custom entities within text. Essential for information extraction, document processing, and knowledge graph construction. Our annotators maintain consistent entity boundaries and handle ambiguous cases with expert judgment.
Sentiment Analysis & Opinion Mining
Classify text as positive, negative, neutral, or mixed sentiment at document, sentence, or aspect level. Perfect for brand monitoring, customer feedback analysis, and social media intelligence. We capture nuanced emotions and context-dependent sentiment shifts.
Text Classification & Categorization
Assign single or multi-label categories to documents, emails, support tickets, or social media posts. Used for content moderation, topic modeling, document organization, and automated routing systems. Consistent taxonomy application across large datasets.
Intent Classification
Label user queries and conversational text with specific intents for chatbot training, voice assistant development, and customer service automation. We understand context, handle ambiguous requests, and identify multi-intent queries.
Semantic Annotation & Relationship Extraction
Mark relationships between entities, identify subject-verb-object triples, and capture semantic connections within text. Critical for question-answering systems, knowledge bases, and advanced NLP research.
Part-of-Speech (POS) Tagging
Annotate grammatical roles of words for linguistic research, machine translation, and syntactic analysis. Available across multiple languages with native speaker validation.
Text Summarization & Keyphrases
Create reference summaries and extract key phrases for training abstractive and extractive summarization models. Human-quality summaries that capture core meaning without losing context.
Conversational AI & Dialogue Annotation
Label dialogue acts, speaker turns, topic shifts, and conversational context for chatbot training. Includes slot filling, dialogue state tracking, and multi-turn conversation flow annotation.
Machine Translation Quality Assessment
Evaluate translation accuracy, fluency, and adequacy. Post-editing and error annotation for improving MT systems across language pairs.
120+ Multilingual Capabilities
Unlike monolingual annotation vendors, AI Taggers provides native-speaker text annotation across 120+ languages.
Major Languages
English, Spanish, French, German, Italian, Portuguese, Russian, Japanese, Korean, Chinese (Simplified & Traditional)
Middle Eastern
Arabic (Modern Standard & Dialects), Hebrew, Persian, Turkish, Urdu
South Asian
Hindi, Bengali, Tamil, Telugu, Punjabi, Marathi, Gujarati, Malayalam
Southeast Asian
Vietnamese, Thai, Tagalog, Indonesian, Malay, Burmese, Khmer
African
Swahili, Amharic, Yoruba, Zulu, Hausa, and more
European
Dutch, Swedish, Norwegian, Danish, Finnish, Polish, Czech, Greek, Romanian
Every project includes native speakers who understand cultural context, idiomatic expressions, and regional variations.
Australian-Led Quality Assurance
AI Taggers maintains enterprise-grade accuracy through rigorous human-in-the-loop workflows.
Multi-stage verification process
Annotator → Senior reviewer → Quality auditor pipeline ensures every label meets your specifications.
100% human-verified annotations
No automated pre-labeling shortcuts. Real linguists and domain experts validate every annotation.
Inter-annotator agreement (IAA) tracking
We measure and report Cohen's Kappa and Fleiss' Kappa scores to ensure consistency across annotator teams.
Continuous calibration sessions
Regular alignment meetings keep annotation quality high as your guidelines evolve.
Edge case resolution
Our QA teams flag ambiguous text and collaborate with your team to resolve annotation challenges.
Scalability for Enterprise NLP Projects
Start with a pilot batch to validate our process, then scale to massive datasets without quality degradation.
Text samples annotated
Languages supported
Global annotation teams
Industries We Serve
Healthcare & Medical NLP
Clinical notes annotation, medical entity extraction, drug-disease relationship labeling, and patient record de-identification.
Financial Services
Financial document analysis, earnings call transcription annotation, regulatory compliance text classification, and sentiment analysis of market commentary.
E-commerce & Retail
Product review sentiment, customer feedback categorization, search query intent, and product attribute extraction.
Legal & Compliance
Contract clause identification, legal entity recognition, case law annotation, and regulatory document classification.
Customer Support & CX
Support ticket categorization, chatbot training data, intent classification, and customer sentiment tracking.
Social Media & Content Moderation
Hate speech detection, content policy violation labeling, toxicity classification, and community guideline enforcement.
EdTech & Language Learning
Grammar error annotation, language proficiency assessment, reading comprehension datasets, and linguistic feature labeling.
Government & Defense
Intelligence document processing, multilingual threat detection, propaganda identification, and classified text annotation.
Why NLP Teams Choose AI Taggers
Linguistic expertise
Native speakers and trained linguists who understand grammatical nuance, cultural context, and domain-specific terminology.
Annotation guideline development
We collaborate with your team to create clear, unambiguous guidelines with edge case examples before annotation begins.
Transparent quality metrics
Regular reporting on inter-annotator agreement, error rates, and annotation velocity throughout your project.
Secure & compliant workflows
Australian data oversight, NDAs, and secure annotation platforms for sensitive text data.
Format flexibility
Deliver in JSON, XML, CSV, CoNLL, BRAT, or your custom format requirements.
Our Text Annotation Process
Consultation & Guidelines Development
We review your text data, NLP objectives, and annotation schema. Our team develops comprehensive guidelines with annotated examples.
Pilot Batch Annotation
Annotate 500-1,000 samples as a quality test. You review results, we measure IAA scores, and we refine guidelines together.
Full-Scale Production
Distributed annotation teams begin labeling with real-time QA monitoring. Weekly quality reports track accuracy and consistency metrics.
Delivery & Continuous Improvement
Receive annotations in your preferred format. We incorporate feedback, resolve edge cases, and improve as your model requirements evolve.
Real Results From AI Teams
"AI Taggers delivered consistent NER annotations across 6 languages where our previous vendor struggled with quality."
NLP Lead
Global Tech Company
"The sentiment annotation accuracy was exceptional, especially for handling sarcasm and context-dependent emotion."
AI Research Scientist
Healthcare Analytics Firm
Get Started With Expert Text Annotation
Whether you're building chatbots, training sentiment classifiers, or extracting entities from multilingual documents, AI Taggers delivers the annotation quality your NLP models need.
Questions about text annotation?
What annotation types does your NLP model require?
How many text samples need labeling?
What languages are in your dataset?
Do you have existing annotation guidelines?
Our team responds within 24 hours with a tailored solution for your NLP project.