Text annotation assigns agreed labels to language data so that a team can train, evaluate or inspect a system. An Amharic project needs guidelines and review examples suited to the actual text, not an English task translated word for word. Zoha can help scope the dataset, pilot and handover requirements from Addis Ababa.
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Name the decision the dataset should support
A text-labeling request might concern a customer's intent, a named item in a sentence or the relevance of a response. These are different tasks with different instructions. Begin with a short description of what the receiving system will do and a sample of the text it will encounter.
Decide whether the reviewer labels an entire message, a sentence or a selected span. A complaint can contain both a service request and a question. The guidelines need to say whether one label, several labels or an exception is appropriate.
Include the language people actually use
An Amharic dataset may include mixed scripts, abbreviated wording, quoted text and messages that switch languages. Build the pilot from the material you expect to process. Do not clean away those features unless the task explicitly requires normalization.
The scope should identify the language and domain review skills needed. A technical term, a regional expression and a misspelling may look similar to someone without the right context. The pilot is the place to test those decisions before estimating a larger engagement.
Make boundary cases part of the instructions
| Example decision | What the guideline should specify |
|---|---|
| A message has two intentions | Whether multiple labels are allowed and how precedence works |
| A name includes punctuation | Which characters belong inside the marked span |
| A sentence quotes another person | Whether the label concerns the author or the quoted content |
| The meaning is uncertain | Which review flag to use and who resolves the question |
These are illustrative decision types. The actual labels should come from your task. A small set of accepted and rejected examples is often easier to apply than a long list of abstract definitions.
Review disagreements without hiding uncertainty
Have the pilot reviewed against one instruction version. Record where decisions differ and whether the cause is a missing rule, a language question or an inconsistent application. A task owner should resolve definition changes and keep the decisions in the guidance.
Do not turn every uncertain item into a forced label simply to finish a batch. An uncertainty or exclusion field can protect the dataset from false certainty. Keep the original text, its identifier and the review decision together so that changes remain traceable.
Specify a useful dataset handover
- Retain stable text identifiers and the agreed unit of annotation.
- Document the label definitions, instruction version and accepted output structure.
- Keep unresolved language or task questions in a separate exception file.
- Test a small export in the receiving workflow before delivering the full collection.
The estimate depends on text length, label complexity, specialist terminology, ambiguity and review depth. Agree those inputs using a pilot rather than assuming a fixed number of messages tells you the amount of work. Language coverage, tooling and batch size belong in the actual project scope.