Text annotation
Optimize your text data for NLP and LLM. Our text annotation services ensure accurate and relevant structuring, ensuring high-quality datasets to train and perfect your advanced language models.
🧠 Language structuring
NER, classification, relationship extraction, feeling analysis: we give meaning to your texts to train your NLP or LLMs models.
🧾 Sectoral control
Health, legal, finance, customer service: our annotators understand business specificities and adapt their work to your field.
✍️ Reliable language annotation
Terminological consistency, semantic segmentation, human review: we ensure quality text annotation, ready for AI.
Annotation techniques
Semantic labeling and NER
Semantic labeling, of which named entity recognition (NER) is a particular case, consists in identifying and classifying text segments according to their meaning (people, places, dates, dates, organizations, quantities, etc.). This is a key step in natural language processing.
Choice of relevant categories (e.g. PERSON, ORGANIZATION, ORGANIZATION, LOCATION, 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
Automatically assign one or more categories to textual content. This task is essential for organizing, filtering, or analyzing large volumes of textual data, whether it's emails, reviews, documents, or online publications.
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
Grammatical and syntactic analysis
Identify the linguistic structure of a text, by assigning to each word its grammatical category (noun, verb, adjective, etc.) and by revealing the relationships between the elements of the sentence (subjects, complements, proposals, etc.).
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
Annotation of intentions and feelings
Enrich textual (or vocal) data by identifying the emotion, tone, or objective expressed by the user. It is essential for training AIs that can understand the emotional or functional context of a message.
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 annotation
Label textual or audio content in several languages, taking into account the linguistic, cultural and syntactic specificities specific to each language. It is essential for the development of AI models capable of understanding and processing data in an international or multicultural context.
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, with case management of Code-switching or misaligned duplicates
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
LLM training data
Design and structure large quantities of rich and diverse textual data to train large-scale language models. These data sets must be coherent, representative and adapted to the objectives of the model (generation, understanding, dialogue, etc.).
Identify the targeted skills: text comprehension, fluid generation, logical reasoning, dialogue, translation, etc.
Gather data from a variety of sources (articles, forums, dialogues, dialogues, legal bases, 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 for LLM generalists — Creation of massive data sets for multilingual, multitasking or open models
RAG (Retrieval-Augmented Generation) — Creation of indexable corpora used to feed hybrid research + generation models
Ongoing evaluation of models — Use of test games from the training game 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 ?
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 use all the data annotation platforms of the market to adapt us to your needs and your most specific requests!
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.
Feed your AI models with high-quality training data!
