Text annotation services that teach models what words mean Entities, categories, and relations tagged to your guide and checked by people
Text annotation for machine learning, to your guide Damco tags the documents, chats, and reviews your language model learns from
How our text annotation process works From sample documents to a tagged corpus your model can train on
- Week 1Write the guideWe tag a sample with you and list every category and entity.
- Week 2Run a pilotYou review a pilot, and we settle the tags reviewers disagreed on.
- From week 3Annotate at volumeLanguage models tag first, and reviewers decide what they doubt.
- Every batchAudit and deliverA second reviewer checks a sample before the batch owner signs.
When to outsource text annotation And when your own subject experts should tag the text
A good fit when
- You have thousands of emails, chats, or contracts to tag
- A language model tags most text well, but someone must check it
- Your categories are stable enough to write down
Look elsewhere when
- Every document needs a lawyer's or a doctor's judgment
- You need a handful of documents tagged once
- Your categories change every week
Text annotation techniques in detail What each technique teaches a language model
Text categorization
We sort each document, email, ticket, or message into the categories you define, such as complaint, refund request, or address change. Clear categories with examples are what let two reviewers agree.
Named entity recognition and entity linking
Named entity recognition tags the people, companies, products, places, dates, and amounts in text. Entity linking then ties each one to a record, so the model knows that “Acme Corp” and “Acme” are the same customer.
We also mark how the parts of a sentence relate, such as who sold what to whom.
Semantic annotation and phrase chunking
Semantic annotation tags words with the concept they stand for, such as a symptom, a part number, or a policy type. Phrase chunking marks the parts of speech and groups words into phrases, so a model reads an expression as a whole.
Sentiment and intent annotation
We tag each review, post, or chat message as positive, neutral, or negative, and with the intent behind it. Sarcasm and mixed feelings go to a person by default.
Metadata labeling
Metadata labeling adds descriptive fields to text, such as document type, author, language, and date. It makes a large corpus searchable and lets your team pick the right slice to train on.
Text annotation for LLM training
Large language models need curated text too, such as answers ranked by quality, answers checked against facts, and content flagged for safety. We run the same guide, review, and audit for this work.
Questions about text annotation services What teams ask before they outsource text annotation
What is text annotation in machine learning?
Text annotation adds labels to text so a model can learn from it, such as categories, entities, sentiment, intent, or relations. It's how a support bot learns that two different sentences ask for the same refund.
Which text annotation tools do you use?
We work in your tool, or in an open-source tool such as Label Studio or doccano. Language models draw the first tags, and reviewers correct them in the same tool.
How do you handle sensitive documents?
We agree with you who may see the text, where it's stored, and when it's deleted. Where you need it, we remove names and account numbers before annotation starts.
How is text annotation priced?
We price per document, per message, or per entity, depending on the technique. The price comes from a pilot on your own text.
Send us a sample of the text and your tags
We'll reply with how we'd run the pilot and the price per document.