Tech & AI Solutions
Data Annotation & Language Data Services
Human language expertise applied to the data that AI systems need to understand language.
AI systems that process language need training data that is accurate, consistently annotated, and representative of the language as it is actually used. We provide language data annotation and preparation services supported by linguists with the subject-matter knowledge required to produce high-quality labelled data.
Language Data Built by Language Experts
Language model training, AI evaluation, and natural language processing applications depend on data that has been labelled, classified, or reviewed by people who understand the language in depth. Annotation produced by annotators without the relevant linguistic and cultural knowledge creates data quality problems that affect the systems trained on it.
We provide data annotation and language data services for organizations building or improving AI and NLP systems, contributing the linguistic expertise required to produce accurate, consistent, and contextually appropriate labels across Arabic and other languages.
What's Included
A complete approach to data annotation & language data services
Text Annotation
Labeling of text data according to defined annotation schemas for named entity recognition, sentiment, intent, and other classification tasks.
Classification
Classification of text samples into defined categories based on content, language, intent, or quality criteria relevant to the AI application.
Linguistic Labeling
Linguist-produced labeling of language data for part-of-speech, dependency, morphology, and other linguistic features requiring expert knowledge.
Dataset Review
Review of existing annotated datasets for consistency, accuracy, and adherence to the annotation guidelines before they are used in training.
Quality Control
Structured quality control processes applied to annotation tasks, including inter-annotator agreement review and adjudication of disagreements.
Language Data Preparation
Preparation and cleaning of raw language data for annotation, including deduplication, normalization, and format standardization.
Process
How it works
Task and Data Review
We review the annotation task, data characteristics, guidelines, quality requirements, and the AI application the data is intended to support.
Annotator Selection and Training
Annotators with the appropriate linguistic background and subject-matter expertise are selected and trained on the annotation guidelines.
Annotation and Classification
The annotation task is executed with regular quality sampling to verify consistency and accuracy against the guidelines.
Quality Review and Adjudication
Annotated data is reviewed for consistency and accuracy. Disagreements between annotators are resolved through defined adjudication processes.
Delivery and Documentation
Annotated data is delivered in the required format with documentation of the annotation guidelines and quality metrics for the dataset.
Why Randa Translation
What sets our approach apart
Linguist-Led Annotation
Annotation tasks requiring linguistic expertise are performed by qualified linguists, not general-purpose crowd workers.
Arabic Language Expertise
We have specific capability in Arabic language annotation, including dialectal variation, morphological complexity, and cultural context.
Consistent Quality Control
Structured inter-annotator agreement review and adjudication ensure data consistency across large annotation batches.
Aligned to AI Application
We review the intended AI application to ensure annotation decisions reflect how the data will actually be used in training or evaluation.
Related Industries
Build AI Systems That Understand Language as It Is Spoken
Tell us about your annotation task, target languages, data characteristics, and quality requirements. We will design the right approach and assign linguists with the relevant expertise.