Text Annotation

What is Text Annotation?

Text annotation is the process of highlighting and labeling pieces of text so machines can learn what’s important—like identifying people, places, products, or emotions. Our trained linguists and annotators tag key information, enabling NLP models to detect language patterns and understand the context of conversations or content.

Why Choose LabelCo.Ai?

• 100% human-in-the-loop accuracy

• Domain-trained annotators & linguists

• Secure workflows with NDAs and data privacy

• Tool flexibility (yours or ours)

• Scale-ready for enterprise and startup needs

150+

250+

Annotators Onboarded

Happy clients

Our 5-Step Process

3. Tools Set-Up and Labelling Guidelines

We configure the annotation tool (yours or ours) and set clear, consistent instructions for our annotators to follow.

4. Human in Loop Annotation

Our trained annotators tag the text according to the guidelines, ensuring accuracy and consistency across every data point.

2. Define Annotation Goals

We work with you to define exactly what needs to be labeled—like sentiment, intent, entities, or categories—based on your model's purpose.

Every annotation goes through a multi-layer quality check. Once verified, we export your labeled dataset in the required format—ready to train your NLP model.

5. Quality Review and Final Delivery

At LabelCo.Ai, our expert annotators and AI tools work together:

1. Text is Collected

We begin by gathering the raw data—emails, reviews, chats, documents—and clean it to remove irrelevant or duplicate content.

Named Entity Recognition (NER)

What it is: We tag names of people, places, organizations, dates, and more.
Why it matters: Helps AI extract real-world information from text.
Where it’s used: Search engines, resume parsing, chatbots.

Sentiment and Emotion Detection

What it is: We label opinions as positive, negative, neutral—or even emotional states like happy, angry, or sad.
Why it matters: Helps brands track public sentiment and improve customer experience.
Where it’s used: Product reviews, social media monitoring, support tickets.

Intent Detection

What it is: We help AI understand the purpose behind a message—whether someone is asking, complaining, or trying to buy.
Why it matters: Boosts chatbot intelligence and improves user interactions.
Where it’s used: Virtual assistants, voice AI, customer service bots.

Text Classification & Categorization

What it is: We assign labels or categories to blocks of text based on topics or themes.
Why it matters: Helps AI filter and sort large volumes of text data.
Where it’s used: News feeds, content moderation, e-commerce tagging.

Part of Speech Tagging

What it is: We label words by their grammatical roles—like nouns, verbs, adjectives.
Why it matters: Enables deeper language understanding for grammar-based models.
Where it’s used: Linguistic AI, grammar checkers, translation systems.

Common Use Cases

1. Healthcare & Life Sciences

Use Case:

• Annotating electronic health records (EHRs), doctor’s notes, clinical trial data, and prescriptions.

• Labeling symptoms, diseases, drug names, dosages, and patient details.

Applications:

• Medical NLP for diagnosis assistance

• Clinical decision support

• Automated summarization of patient history

2. Finance & Banking

Use Case:

• Tagging financial entities like currencies, account types, transactions, and terms in documents.

• Annotating customer service chats for sentiment and intent.

Applications:

• Fraud detection

• Automated compliance checks (AML/KYC)

• Customer experience analytics

3. Legal Services

Use Case:

• Annotating legal clauses, contract types, named entities (people, laws, dates).

• Highlighting obligations, risks, or deadlines in legal texts.

Applications:

• Contract analysis tools

• Case law summarization

• Legal document classification

4. E-commerce & Retail

Use Case:

• Labeling product descriptions, customer reviews, and search queries.

• Annotating sentiment and intent in user feedback.

Applications:

• Personalized search and recommendation systems

• Product classification and query understanding

• Review moderation and insights

5. Customer Support & Chatbots

Use Case:

• Tagging intents (refund, order status), sentiment, and named entities (order number, product).

• Annotating FAQs and support interactions.

Applications:

• Training virtual assistants and chatbots

• Automating ticket routing

• Improving self-service accuracy

6. Social Media & Content Platforms

Use Case:

• Annotating text content for toxic speech, hate speech, fake news, and emotional tone.

• Tagging trending topics and hashtags.

Applications:

• Content moderation

• Trend tracking

• Sentiment-driven marketing

7. Education & EdTech

Use Case:

• Tagging topics, difficulty level, and subject areas in learning materials.

• Annotating student queries and essays.

Applications:

• Smart tutoring systems

• Learning recommendation engines

• Automated grading and feedback

8. SaaS & Enterprise Tools

Use Case:

• Labeling user feedback, bug reports, and support tickets.

• Annotating user behavior logs or survey responses.

Applications:

• Product experience analysis

• Feature prioritization based on feedback

• Automated insights generation

Frequently asked questions

What types of text annotation does LabelCo.Ai offer?

Answer:
We provide a wide range of annotation services, including:

• Named Entity Recognition (NER)

• Sentiment & emotion labeling

• Intent detection

• Text classification

• Part-of-speech tagging

• Entity linking

• Relationship annotation

• Custom schema-based tagging

Do you support multilingual text annotation?

Answer:
Yes. LabelCo.Ai offers annotation in over 60+ languages, ensuring accuracy and consistency for global NLP applications.

Can you work with my in-house tools or APIs?

Answer:
Absolutely. We offer flexible integration options—we can use your annotation tools or provide access to our own secure, scalable platform. We also support API-based workflows for streamlined data exchange.

How do you ensure quality in text annotation?

Answer:
We follow a rigorous human-in-the-loop (HITL) model with multi-tiered QA checks, consensus labeling, reviewer feedback, and periodic audits. Our annotators are domain-trained and work with client-approved guidelines.

Can you handle domain-specific annotation (like legal or medical)?

Answer:
Yes. We specialize in domain-specific annotation projects and assign subject matter experts when needed. We follow industry terminology and can adapt to your custom schemas.

Is my data secure with LabelCo.Ai?

Answer:
Yes. We follow strict data protection protocols, sign NDAs, and ensure compliance with standards like GDPR, HIPAA, and SOC 2. All data is securely stored and handled by trained professionals.