Data Labeling for NLP with Real-life Examples
NLP technology is increasingly being used to enable smart communication between people and their devices. Companies like Google, Amazon, and OpenAI have invested billions in NLP technologies that can understand, interpret, and generate human language with remarkable accuracy. However, behind every sophisticated NLP model lies an important foundation: labeled training data.
See the critical role of data labeling in NLP development and the practical insights for businesses:
What is NLP data labeling?
NLP data labeling is the process of annotating text, audio, or multimodal data with meaningful tags, categories, or structures that machine learning models can learn from. This involves human experts and automated systems working together to identify patterns, relationships, and semantic meanings within language data.
The process typically combines:
- Automated pre-labeling: Machine learning models provide initial annotations with confidence scores
- Human verification: Expert annotators review, correct, and enhance machine-generated labels
- Quality assurance: Multiple review layers ensure annotation consistency and accuracy
- Iterative improvement: Labeled data is fed back into models to improve future automated labeling
Data labeling is an integral part of training NLP models to mimic the human ability to understand and generate speech. The landscape of NLP data labeling has evolved significantly, incorporating generative AI, advanced machine learning techniques, and sophisticated human-AI collaboration frameworks.
How does it work?
Building a highly accurate NLP model requires a high volume of training data. This is because NLP models are based on machine learning, a subdomain of artificial intelligence (AI), and modern machine learning approaches like deep learning are data-hungry. To generate this large volume of training data, companies rely on:
- Machine learning models in cases where labeling can be automated
- Humans, in cases where machine learning models do not have high confidence.
In this model, data that can not be auto-labeled with high confidence is sent by the ML model for human labeling. The data labeled by humans is then sent again to the ML model to retrain and improve it.
To learn more about how NLP data annotation works, check out this video:
The Role of GenAI, Machine Learning Models, and Human Annotators
Generative AI in Data Labeling
The emergence of large language models like GPT-4, Claude, and Gemini has revolutionized data labeling workflows. Companies like Anthropic and OpenAI now offer AI-assisted labeling that can:
- Generate synthetic training data for rare scenarios
- Provide consistent initial annotations across large datasets
- Handle multiple languages simultaneously
- Create contextually relevant examples for edge cases
Example: Spotify uses generative AI to automatically label podcast content for better categorization and recommendation systems, reducing manual labeling time by 70% while maintaining quality standards.1
Machine Learning Models
Modern NLP data labeling employs active learning and human-in-the-loop systems where:
- Uncertainty sampling: Models identify data points they’re least confident about for human review
- Diverse sampling: Algorithms select representative samples to maximize learning efficiency
- Disagreement resolution: Multiple models vote on annotations, flagging disagreements for human arbitration
Example: Microsoft’s Cognitive Services uses active learning in their text analytics API, where the model continuously identifies uncertain predictions and routes them to human annotators, improving accuracy by 15% while reducing labeling costs by 40%.2
Human Annotators
Despite AI advances, human annotators remain essential for:
- Cultural and contextual nuances: Understanding idioms, sarcasm, and cultural references
- Domain expertise: Medical, legal, and technical terminology requiring specialized knowledge
- Ethical considerations: Identifying bias, harmful content, and sensitive information
- Quality control: Establishing annotation guidelines and maintaining consistency
Example: Amazon’s Alexa team employs linguists and domain experts from over 30 countries to ensure their voice assistant understands regional dialects, cultural contexts, and local expressions accurately.3
What are the types of data annotation in NLP?
Utterance annotation
In spoken language analysis, utterances are the smallest pieces of speech. Anything that a user says that starts and ends with a pause is considered an utterance. Utterance annotation involves segmenting continuous speech or text into meaningful units for processing.
For example:
“What’s the weather like in Tokyo today?” (single intent utterance)
“Book me a flight to Paris… actually, make that Rome instead” (multi-turn utterance with correction)
“Uh, I need to, you know, cancel my subscription” (utterance with disfluencies)
Since much of the language data is in long sentences, the data is split into individual utterances for training the ML model.
Intent annotation
This refers to the predictions drawn by the ML from the user’s utterance. Intent annotation identifies the purpose behind user statements, enabling systems to take appropriate actions.
Common intent categories:
- Informational: “What time does the store close?”
- Transactional: “Order me a large pizza.”
- Navigational: “Take me to the nearest gas station.”
- Conversational: “How are you doing today?”
Example: Salesforce’s Einstein AI uses intent classification across its CRM platform, helping sales teams automatically categorize and route over 50 million customer interactions monthly, improving response times by 45%.4
There are also casual intents. For instance, a simple “yes” can also be “yeah, sure.” For more on intent classification, feel free to check our article on the topic.
Entity annotation
Entity annotation identifies and categorizes specific objects, people, places, dates, and concepts within text.
Example annotations:
- “I want to fly from New York [LOCATION] to London [LOCATION] on December 15th [DATE] for $500 [CURRENCY]”
- “Apple Inc. [ORGANIZATION] released the iPhone 15 [PRODUCT] in September [DATE].”
Example: Netflix uses named entity recognition to automatically tag content with actors, directors, genres, and themes, processing metadata for over 15,000 titles across 190+ countries.5
What are the challenges in NLP data labeling?
Data Preparation and Quality Challenges
Volume and Diversity Requirements: Modern NLP models require massive, diverse datasets. OpenAI’s GPT models are trained on hundreds of billions of tokens, requiring unprecedented data curation efforts.
Data Acquisition Complexity
- Sourcing representative data across demographics, languages, and use cases.
- Ensuring legal compliance and data rights.
- Maintaining privacy while preserving utility.
Example: Twitter (now X) faced significant challenges when training content moderation models, requiring labeled data across 50+ languages and cultural contexts to ensure fair enforcement of community guidelines.6
Linguistic Challenges
Multilingual Complexity: Different languages present unique challenges:
- Arabic: Right-to-left text, contextual letter forms.
- Chinese: Character-based writing, tonal variations.
- German: Compound words, complex grammar structures.
Case study: DeepL, competing with Google Translate, invested heavily in multilingual data labeling, employing native speakers for 31 languages and achieving 3x better accuracy than competitors for European language pairs.7
Emotional and Contextual Understanding
- Sarcasm detection: “Great, another Monday” (negative despite positive word).
- Mixed emotions: “I love this product, but the price is killing me.”.
- Cultural sentiment variations: Expressions that are positive in one culture but neutral in another.
Example: TikTok’s content moderation system processes over 1 billion videos daily, using sentiment analysis trained on culturally-specific labeled data to understand context across 150+ countries and multiple age demographics. 8
Industry-Specific Challenges
- Healthcare: HIPAA compliance, medical terminology accuracy
- Legal: Precedent understanding, jurisdiction-specific language
- Finance: Regulatory compliance, risk assessment terminology
Technical Terminology Management: Each industry requires specialized knowledge:
- Automotive: “Torque vectoring,” “regenerative braking”.
- Fashion: “Chambray,” “silhouette,” seasonal trend terminology.
- Technology: API documentation, programming language specifics.
Example: IBM Watson Health required over 2 million hours of medical expert annotation to train its oncology decision support system, involving oncologists, pharmacists, and medical researchers to ensure clinical accuracy.
Scale and Resource Management
Annotation Consistency at Scale
- Inter-annotator agreement: Ensuring multiple annotators label similar content consistently.
- Guidelines: Updating annotation standards as models improve.
- Quality control: Implementing review processes without bottlenecks.
Cost vs. Quality Balance
- Expert annotation costs: Medical/legal experts can cost $100-200/hour.
- Crowd-sourcing quality: Balancing cost savings with accuracy requirements.
- Time constraints: Meeting project deadlines while maintaining standards.
Ethical and Bias Challenges
- Demographic bias: Ensuring representation across age, gender, and ethnicity.
- Geographic bias: Including diverse regional perspectives.
- Socioeconomic bias: Representing different economic backgrounds.
Example: Amazon had to scrap its AI recruiting tool in 2018 after discovering it was biased against women, highlighting the critical importance of diverse, unbiased training data in NLP systems.9
To outsource or not to source
Outsourcing
Some companies can choose to outsource through crowd-sourced labeling services. With this option, you can have increased flexibility and quality of data labeling. However, outsourcing can be expensive and can also lead to data leaks.
In-house labeling
Some organizations hire internal labelers and tools for NLP data labeling. This can be a more secure option; however, it takes a dedicated workforce and tools to match the labeling capacity of a third-party service.
How to make the decision?
Answering the following question can help you decide which option to choose:
- What level of expertise is required for your NLP labeling task?
- What level of data privacy do you require?
- What are the required labeling quality and capacity?
- Will NLP data labeling be a core part of your business?
To make an objective decision on which option to choose, check out our sortable and filterable list of data annotation, labeling, and tagging services, to compare and select the option that best suits your needs.
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Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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