Generative AI is getting most of the attention, but in real-world deployments, performance and stability are still determined by one thing: data quality. Text data is especially sensitive because it contains meaning and context. If the “ground truth” standard shifts even slightly, outputs from automated classification or document automation can become inconsistent and unreliable. This is where text labeling becomes essential.

Text labeling is an AI data labeling service in which humans read text and apply predefined rules to either (1) assign classification tags or (2) extract key values and structure them into fields. When multilingual data is involved, the difficulty rises sharply—because the same labeling standard must be applied consistently across different languages, expressions, and cultural nuances. That is why multilingual LSPs (language service providers) often take on these projects. In a recent program with a global technology company, Hansem Global supported text labeling for Asian languages and delivered two representative project outcomes, introduced below.

Why Text Labeling Matters: “Locking” the Standard for AI Operations

AI systems learn—and later operate—based on the human-defined ground truth. If labeling rules are unclear or applied inconsistently, the system will reflect that uncertainty.

• If labeling standards vary by annotator, identical inputs may produce inconsistent outputs.
• If language-specific expressions are not handled under a unified standard, performance gaps emerge across locales.
• The more noise in the data (typos, abbreviations, mixed languages, layout differences), the more important it is to define “the right answer” clearly.

In other words, text labeling is not simple manual work. It is data quality operations: defining standards, enforcing consistency, and managing quality end to end.

Common Types of Text Labeling Services

Classification
Classification assigns a category to the meaning of a piece of text.
Examples: inquiry type (billing/shipping/refunds), sentiment (positive/neutral/negative), issue type (error/feature request/complaint)

Extraction / Field Annotation
Extraction pulls specific values from text or documents and maps them into standardized fields.
Examples: extracting employer name, pay period, and amounts from pay stubs; extracting key account and transaction fields from bank statements

Policy / Risk Labeling (optional)
This identifies content risks such as harmful language, spam, or personally identifiable information (PII).
Examples: marking phone numbers/emails/addresses; classifying abusive or hateful content

Other extensions include intent labeling, span labeling, and similarity/duplicate labeling, but the three categories above are the most commonly used foundations in production environments.

Case A: Sentiment Labeling for Conversational Data (Classification)

The first project involved labeling the sentiment tone of each turn in service conversation data. The key point is that the sentiment is assessed per turn—not based on the overall mood of the entire conversation.

• Five levels: very positive / positive / neutral / negative / very negative
• Simple courtesy expressions like “Thank you” are treated as neutral
• Issue reporting without explicit dissatisfaction may be labeled as neutral
• Noise (meaningless strings, mixed-language fragments) follows separate exception rules

This type of labeling can support operational monitoring (e.g., tracking negative-turn ratios or detecting spikes in specific complaint patterns). It can also serve as a decision signal in customer-facing automation, helping systems choose the appropriate response tone—such as providing guidance, asking clarifying questions, or acknowledging frustration.

Case B: Document Information Extraction (Extraction / Field Annotation)

The second project focused on extracting required values from documents such as pay stubs and bank statements and entering them into standardized fields. The defining feature of this work is that it is not “interpretation,” but accurate reproduction.

• Values are copied exactly as written—no guessing or assumptions
• If a value is missing, it is recorded as “NA,” not left blank
• Case sensitivity, numeric formatting (including trailing zeros), and currency symbols are preserved
• Synonyms and format variations are managed so different document wording maps to the same standardized field

For example, the same concept may appear as “Net Pay,” “Take-home Pay,” or “Amount Paid” depending on the document. Labeling aligns these variations into a single standardized field so values can be captured consistently across document types and formats. This ground-truth dataset connects directly to document-based workflow automation in areas such as KYC, underwriting, reconciliation, verification, and compliance operations.

What Both Projects Share: Text Labeling Is Quality Operations

Although the two cases are different in format, the core is the same: humans fix a correct answer according to rules, and that answer becomes the operational standard for automation. Success depends on three elements:

1. Definition: clearly specifying what counts as the “correct answer,” including exceptions
2. Standardization: applying the same rule consistently across languages, document types, and expression variants
3. Quality Assurance: maintaining inter-annotator consistency, managing edge cases, and delivering reproducible outputs

Text labeling projects are challenging because quality can easily become dependent on individual annotators. Hansem Global reduces that variability through a structured operating model led by internal AM/PMs from kickoff to delivery—covering guideline alignment, training, workforce allocation, issue handling, and final delivery. Hansem also assigns resources based on difficulty using a tiered staffing approach (L1–L3), and applies a three-step review process (QA–Review–LQA) for consistency. For security and governance, projects are supported by an ISO 27001-based security framework. In short, Hansem’s differentiation is not individual annotator skill—it is an operating system designed to make quality repeatable.

Conclusion: Text Labeling Is the Last Foundation for Making AI Work in the Field

Sentiment labeling enables customer experience monitoring and more consistent service responses. Extraction labeling makes document-driven automation practical. Both ultimately serve the same purpose: building reliable ground truth.

If you are considering text-based automation—such as automated classification, VOC analytics, or document processing—start by reviewing how you define labeling standards and how you operate quality. In multilingual environments where consistency and security requirements must be met simultaneously, the deciding factor is often not the labeling task itself, but the operating system behind it.