Text annotation tool

Text annotation tool

Kili Technology’s text annotation tool helps you to build training datasets faster and solves NLP machine learning challenges that will smoothen your organisation’s workflow. Whether it’s chatbot utterances, emails, medical reports, the voice of customers, complex patents and other raw text. Our text annotation software covers all major labelling tasks.

pdf text annotation labeling classification named entity recognition

Our partners already deployed successfully thousands of AI projects into production thanks to Kili.

Fast Text Annotation Software and Labelling Tool

From simple conversations classification to advanced multi-tasks interfaces with named entities recognition and relations extraction, all-natural language processing use cases are covered. Each text annotation interface is designed to support productivity. Composability of each text labelling interface allows adapting to the specific needs of each project.

For all tasks: text classification single class, multiclass, dropdown for the long list, nested classification to manage complex ontologies. Named entity recognition, relation extraction, Optical Character Recognition (OCR).

File types: txt or PDF.

Text Labelling and Annotation Software For Best Text Data Quality

Kili Technology’s online or on-premise text labelling tool eases collaboration with domain experts or external labellers and data science teams. Labelled data quality monitoring is built-in thanks to advanced tools like consensus analysis, honeypot, review, instructions and more.

Simple collaboration between labellers & data-scientists

Kili technology’s annotation online tool for text is designed to ease collaboration, e.g. on-board domain expert or external labelling teams, leverage better data governance tools, advanced asset queue management distribution when it comes to labelling a large volume of data at once.

labeling collaboration text data

Work on a robust text annotation platform

Our platform is available online or on premise. For the community, we developed recipes, made open source on our Github. It makes it easy to leverage our powerful GraphQL API to interact programatically with the tool. It also allows to leverage the open source ecosystem: AutoSKlearn (open source autoML framework of SickitLearn) to train online a text classification model; Snorkel to implement weakly supervised learning strategies: for name entities recognition and classification, it allows to combine simple rules like dictionnaries or REGEX to build powerful pre annotators. Our wrapper in Python makes it even easier if your are not familiar with GraphQL.

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