Language models and NLP systems are only as good as the text examples they learn from. A support chatbot needs messages labeled by intent. A contract tool needs clauses and parties tagged. A sentiment model needs reviews rated consistently. When different people label the same text differently, the model learns confusion instead of patterns.
Our text annotation services deliver consistent, accurate labels for NLP and LLM projects. We define clear guidelines with you, label a sample batch for your review, and scale with multi-step review — covering classification, entities, sentiment, intent, relations, and more.
What are text annotation services? Text annotation services label written content so natural language processing (NLP) and large language models can learn from it. Common tasks include text classification, named entity recognition (NER), sentiment and emotion labeling, intent tagging, relation extraction, entity linking, part-of-speech tagging, and content moderation. Providers write guidelines, train annotators, check agreement, and deliver labels in formats such as JSONL or CSV.
Text Annotation at a Glance
Question | Answer |
|---|---|
What it is | Labeling text for NLP and LLM training and evaluation |
Task types | Classification, NER, sentiment, intent, relation extraction, entity linking, moderation, summarization review |
Content types | Support tickets, chats, emails, reviews, social posts, documents, contracts, search queries |
Output formats | JSON, JSONL, CSV, spaCy, CoNLL, or your custom schema |
Tools | Label Studio, Doccano, or your existing annotation platform |
How to start | A sample batch on your own text, reviewed by you before scaling |
Text Annotation Types
Type | What annotators do | Typical use |
|---|---|---|
Text classification | Assign categories to a whole text | Ticket routing, spam detection, topic tagging |
Named entity recognition | Tag names, organizations, dates, amounts, products | Information extraction, search |
Sentiment and emotion | Label positive, negative, neutral, or specific emotions | Review analysis, customer feedback |
Intent labeling | Identify what the user wants | Chatbots and virtual assistants |
Relation extraction | Link entities and describe their relationship | Knowledge graphs, contract analysis |
Entity linking | Connect mentions to records in a reference list | Product matching, customer matching |
Content moderation | Flag text against content policies | Community safety, trust and safety |
Common Use Cases
Customer support — intent, urgency, product, and sentiment tags on tickets and chats
Chatbots and assistants — intent and entity labels for training and testing
Contracts and legal documents — parties, dates, obligations, and clause types
Reviews and feedback — sentiment, topics, and feature mentions
Search and e-commerce — query intent and product attribute extraction
Finance and operations documents — amounts, account references, and document categories
LLM evaluation — rating model outputs for correctness and quality
Our Text Annotation Services
Guideline and taxonomy design
We define label sets and taxonomies with you, with clear rules and examples for ambiguous and edge cases — the biggest single driver of consistent labels.
Annotation at scale
Trained annotators label your text in batches, with progress tracked against deadlines.
Multilingual annotation
We annotate English and other languages based on project requirements, scoped per language before work starts.
Model-assisted labeling
We pre-label text with an existing model or rules and have annotators review and correct the results, speeding up large projects.
Quality and agreement checks
Overlapping items are labeled by several annotators to measure agreement, disputes are resolved by senior reviewers, and gold-standard items track accuracy.
Dataset audit and cleanup
We review existing labeled text for inconsistent or wrong labels, fix them, and update guidelines to prevent repeats.
LLM output review
We rate and correct LLM outputs against rubrics, connecting to our LLM evaluation and RLHF services.
How It Works
Step 1: Share samples and labels
Send sample text and your label set, or let us help design one.
Step 2: Agree guidelines
We write guidelines with examples and agree them with you.
Step 3: Sample batch
We annotate a sample batch and share agreement scores.
Step 4: Scale
Once approved, we scale up and deliver in batches with quality reports.
Step 5: Iterate
We refine guidelines as new cases appear and focus on examples your model gets wrong.
Engagement Models
Model | How it works | Best when |
|---|---|---|
Sample batch | A small labeled set on your text | You're evaluating quality and fit |
Project-based | Priced per item, per entity, or per hour | You have a defined dataset |
Ongoing annotation team | A dedicated team labeling new text continuously | Your model needs regular fresh labels |
Data Security
NDA before any text is shared
Personal data masked before annotation where possible
Work in your environment when required
No reuse of your data
Why Work With Codersarts
Our team builds NLP and LLM applications, so we design label sets and guidelines around how models actually learn and are evaluated. We measure agreement instead of assuming it, and flag when a label set is too ambiguous to produce reliable data.
Frequently Asked Questions
What is text annotation?
Text annotation is labeling written content — with categories, entities, sentiment, intent, or relationships — so NLP models and LLMs can learn from it or be evaluated.
What is named entity recognition annotation?
NER annotation marks spans of text that refer to specific types of things — such as people, organizations, locations, dates, amounts, or products — so models can learn to extract them.
How much do text annotation services cost?
Pricing depends on the task, text length, label set size, language, and volume. Entity and relation tasks cost more than simple classification. We quote after reviewing samples.
How do you keep labels consistent?
Through clear guidelines with examples, calibration rounds, agreement checks on overlapping items, senior review of disputes, and gold-standard items.
Do you annotate languages other than English?
Yes, based on project requirements. We confirm language coverage and reviewer availability during scoping.
Can you handle sensitive text?
Yes. We work under an NDA, can mask personal data before annotation, and can work inside your environment.
What formats do you deliver?
JSON, JSONL, CSV, spaCy, CoNLL, or your custom schema.
Related Data Services
Data Labeling — labeling across all data types
Audio Annotation — transcription and speech labeling
Data Classification — classification models built on labeled text
AI Data Services — data preparation for AI projects
Ready for Consistent Text Labels?
Send us sample text and your label set. We'll propose guidelines and label a sample batch so you can judge the quality yourself.
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