Text Data Annotation Services for NLP and LLMs
Turn unstructured text into accurate, training-ready datasets for NLP and LLM applications. Innovatiana provides managed text data annotation for named entities, classification, relationships, intent, sentiment, linguistic structures and multilingual corpora, with human verification at every stage.
🧠 Structured Text for NLP and LLMs
Named entity recognition, classification, relation extraction, intent and sentiment annotation: we transform raw text into structured labels for NLP pipelines, search systems and language models.
🧾Domain-Trained Text Annotators
Healthcare, legal, finance, technology and customer service: we assign annotators who understand your terminology, document types, label definitions and business context.
✍️ Human-Verified Annotation Quality
Guideline calibration, inter-annotator checks, adjudication and documented quality controls help us deliver consistent, traceable and training-ready text datasets.
Text Data Annotation Services
Named Entity Recognition, Entity Linking and PII Annotation
Identify and label people, organizations, locations, dates, products, amounts and other domain-specific entities within text. We also support entity linking, nested entities, coreference and personally identifiable information (PII) annotation when the use case requires greater semantic depth.
Choice of relevant categories (e.g. PERSON, ORGANIZATION, ORGANIZATION, LOCATION, DATE, PRODUCT, ...) and associated annotation rules
Cleaning, breaking down into relevant sentences or units, and possible anonymization of the content
Manual or assisted selection of text segments corresponding to entities, and assignment of corresponding labels
Cross-reading to verify the accuracy of the annotations and the consistency of the labeling criteria throughout the corpus
Smart search engines — Better understanding of content and intentions through the extraction of key entities
Legal and medical documents — Automatic identification of sensitive entities (persons, pathologies, medications, etc.)
Monitoring and retrieving information — Automatic text analysis to detect trends, alerts or strategic information
Text Classification and Taxonomy Labeling
Assign one or more labels to documents, paragraphs, sentences or messages using a predefined taxonomy. We support single-label, multi-label, hierarchical and multilayer classification for large, diverse text corpora.
Development of a set of relevant classes according to the use case (e.g. positive/negative/neutral, legal/marketing/technical, etc.)
Cleaning of textual data, removal of duplicates, linguistic normalization (punctuation, uppercase letters, special characters, ...)
Assigning categories to each document or sentence by human annotators or using pre-existing tools, with validation
Proofreading and quality control to ensure that the classification criteria are applied uniformly to the entire corpus
Content moderation — Automatic filtering of inappropriate or off-topic messages on forums, social networks or chats
Sorting emails or tickets — Automated routing of incoming requests to the right departments or teams
Sentiment analysis — Assessment of the opinion expressed in customer reviews, surveys or online comments
Linguistic Annotation and Dependency Parsing
Annotate the linguistic structure of text at token, phrase and sentence level. Services include tokenization, lemmatization, part-of-speech tagging, morphology, dependency parsing, constituency, coreference and other language-specific structures.
Breakdown of text into base units (words, sentences) to facilitate analysis
Attribution to each word of its grammatical label (e.g. noun, verb, preposition), taking into account the context
Detection of hierarchical structures: dependencies between words, nominal/verbal groups, subordinates, etc.
Proofreading and validation to correct markup errors and refine analysis in ambiguous or complex cases
Indexing and intelligent search — Better understanding of requests and documents thanks to a detailed analysis of the sentence structure
Automatic text generation — Correct structuring of sentences produced by AI models
Morpho-syntactic labelling — Attribution to each token of its grammatical category, according to the local and global context
Intent, Sentiment, Emotion and Stance Annotation
Label what a user wants, how they feel and what position they express. We annotate intent, sentiment, emotion, tone, urgency, stance and conversational outcomes using label sets calibrated to your product and domain.
Creation of a set of labels adapted to the use case
Cleaning and formatting of texts (or transcripts), anonymization if necessary, segmentation into annotated units
Allocation of labels by annotators according to defined instructions, with the possibility of multi-labelling (e.g.: request for help + frustration)
Cross-validation to ensure consistency of annotations, especially on subtle or ambiguous emotions
Virtual assistants and chatbots — Understanding the intention to adapt responses and propose relevant actions
Reputation monitoring — Detection of emotional trends around a brand or a product
Customizing the user experience — Adapting the tone or content according to the perceived emotion
Multilingual and Domain-Specific Text Annotation
Annotate text across languages, dialects and writing systems with native or fluent teams. We adapt taxonomies and guidelines to linguistic structure, local usage, cultural context, code-switching and domain-specific terminology rather than translating labels mechanically.
Definition of the target languages, the expected level of granularity (morphological, semantic, syntactic...) and the specificities of each language (cultural sensitivity, writing, dialectal variants)
Cleaning and harmonization of data in different languages, coherent segmentation and adaptation to specific scripts (Latin, Arabic, Cyrillic, etc.)
Application of linguistic, semantic or contextual annotation instructions by linguists or annotators who know the native language
Cross-linguistic verification of the coherence and uniformity of annotations across different formats
Machine translation systems — Creation of quality aligned corpora to improve the accuracy of translations
International chatbots — Development of virtual assistants capable of interacting with users in their native language
Comparative analysis between languages — Linguistic, sociolinguistic or sentimental studies on multilingual corpora
Text Annotation for LLM Training and Evaluation
Annotate and review text datasets used to fine-tune, evaluate and monitor language models. We label instructions, responses, factuality, relevance, reasoning quality, safety categories and domain metadata using project-specific rubrics and human review.
Identify the targeted skills: text comprehension, fluid generation, logical reasoning, dialogue, translation, etc.
Gather data from a variety of sources (articles, forums, dialogues, legal databases, technical documents, etc.), ensuring their quality and linguistic and thematic diversity
Elimination of duplicates, correction of errors, filtering of sensitive or irrelevant content, formatting according to the requirements of the model (JSON, txt, XML, etc.)
Adding useful metadata (language, style, register, tone, intention, ...), or generating question/answer pairs, summaries, reasoning chains, etc.
Pre-training of general-purpose LLMs — Creation of massive data sets for multilingual, multitasking or open models
RAG (Retrieval-Augmented Generation) — Creation of indexable corpora used to feed retrieval augmented generation systems
Ongoing evaluation of models — Use of evaluation sets from the training dataset to check performance after each iteration
Use cases
Our expertise covers a wide range of AI use cases, regardless of the domain or the complexity of the data. Here are a few examples:
Why choose Innovatiana for Text Data Annotation?
Our added value
Extensive technical expertise in data annotation
Specialized teams by sector of activity
Customized solutions according to your needs
Rigorous and documented quality process
State-of-the-art annotation technologies
Measurable results
Boost your model’s accuracy with quality data, for model training and custom fine-tuning
Reduced processing times
Optimizing annotation costs
Increased performance of AI systems
Demonstrable ROI on your projects
Customer engagement
Dedicated support throughout the project
Transparent and regular communication
Continuous adaptation to your needs
Personalized strategic support
Training and technical support
Compatible with
your stack
We work in your preferred annotation environment or configure a suitable platform for the project.
Secure Data
We pay particular attention to data security and confidentiality. We assess the criticality of the data you want to entrust to us and deploy best information security practices to protect it.
No stack? No prob.
Regardless of your tools, your constraints or your starting point: our mission is to deliver a quality dataset. We choose, integrate or adapt the best annotation software solution to meet your challenges, without technological bias.
Build Better NLP and LLM Models With Human-Verified Text Data!
