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Label Text for NLP and ML Tasks with our Easy-to-use Text Annotation Tool

Easy-to-use Text Annotation Tool

Build high-quality training datasets with Kili Technology and solve NLP machine learning challenges to develop powerful ML applications. Use your textual data and turn it into high-quality training data regardless of format or structure: emails, medical reports, voice transcripts, complex patents, etc.

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Focus on Training Data Quality Rather than Quantity

Discover how Kili Technology will help you create accurate training data

[1]

Label Efficiently with a Text Annotation Software

Leverage Kili Technology’s text annotation tools to create powerful text-based training datasets easily. Annotate all text-based assets (emails, transcripts, news articles, documentation, etc.) using intent classification, named entity recognition, and relation tasks. Use our powerful labeling queue to prioritize and assign text annotation tasks to specific labelers and reviewers and add validation rules to have their work automatically checked. Finally, run your custom model on the fresh dataset and generate model-based predictions to further accelerate labeling and boost quality.

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[2]

Generate High Quality Text Annotation

Identify the right data to annotate to maximize your model's performance. Streamline collaboration between labelers, reviewers, and MLEs to quickly iterate on your text annotation projects. Minimize inconsistencies in dataset quality by providing continuous feedback. Use our advanced quality metrics to quantify quality and easily pinpoint assets or labelers with low metrics. Leverage our automated QA scripts to programmatically spot errors in your text annotation and use error detection models to improve overall performance.

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[3]

Integrate Text Annotation in Your ML Stack

Safely import data from remote storage (Amazon, Google, or Microsoft cloud storage), track changes to your data, version your projects, and then easily export your labeled text dataset to a preferred format (YOLO, Pascal VOC, Kili, etc.).

Easily manage the entire training data lifecycle of your ML project in Kili. Use specific access levels for your organization members and assign predefined roles (admin, manager, reviewer, labeler) in labeling projects.

Leverage active learning to pre-generate labels. Create a feedback loop between your model and your text annotation project. Use Kili’s Python SDK and API to integrate with all machine learning stacks.

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Leverage a Suite of Quality Text Annotation Data Tools & Services

Everything you need to label at scale and boost the quality of text labels

The right text tooling

All-purpose text tooling with classification & Named Entity Recognition (NER)

Main text formats supported: raw text, rich text, native pdf, etc.

Advanced tools with Named Entity Relationship, transcription & Optical Character Recognition

Support of large text files and documents

Auto ML & pre-labeling for productivity

Refined analytics for data quality

Powerful workflows & advanced queue management

Advanced filtering to spot errors

Automated QA configuration

Native data integration from cloud storage

Advanced automation on labeling ops

Python SDK

SOC 2 compliance

Possibility of on premise data and/or full on premise deployment

Fine-grained access rights management with predefined roles & SSO integration

The right expertise

On demand expert workforce

Full project management

World class support

ML & Data Labelling expert

What is the Best Text Annotation Tool?

Understand what your best fit is

Model assisted labelling

Named-entities automatic propagation

Object annotation (e.g stamp, signature)

PDF support

Formatted text support

Chatbot data support

Complex ontologies

Advanced QA analytics

Programmatic QA

Python SDK & CLI

On-premise data

Hugging Face models

SOC2

Model assisted labelling

Named-entities automatic propagation

Object annotation (e.g stamp, signature)

PDF support

Formatted text support

Chatbot data support

Complex ontologies

Advanced QA analytics

Programmatic QA

Python SDK & CLI

On-premise data

Hugging Face models

SOC2

Model assisted labelling

Named-entities automatic propagation

Object annotation (e.g stamp, signature)

PDF support

Formatted text support

Chatbot data support

Complex ontologies

Advanced QA analytics

Programmatic QA

Python SDK & CLI

On-premise data

Hugging Face models

SOC2

Model assisted labelling

Named-entities automatic propagation

Object annotation (e.g stamp, signature)

PDF support

Formatted text support

Chatbot data support

Complex ontologies

Advanced QA analytics

Programmatic QA

Python SDK & CLI

On-premise data

Hugging Face models

SOC2

Model assisted labelling

Named-entities automatic propagation

Object annotation (e.g stamp, signature)

PDF support

Formatted text support

Chatbot data support

Complex ontologies

Advanced QA analytics

Programmatic QA

Python SDK & CLI

On-premise data

Hugging Face models

SOC2

Labelbox

Labelbox is a data labeling platform created in 2018 that enables text annotation with bounding boxes and other advanced labeling tools. It offers AI-enabled labeling tools, labeling automation, human workforce, data management, and an API for integration.

Scale AI

Scale AI is a service company that has recently developed a platform for annotating large volumes of text.

Scale AI offers pre-labeling with ML models, an automated quality assurance system, dataset management, document processing, AI-assisted data annotation, generation of synthetic data. This data annotation tool supports multiple data formats and can be used for a variety of tasks, including object detection, classification, and text recognition.

UBIAI

UBIAI is a cloud-based solution based in the US that enables annotation of text and documents. They cover the essential tasks of text and document processing like document classification, NER, OCR, and auto-labeling through a NLP-focused user interface. They also support pre-labeling with ML models and different pricing models.

SuperAnnotate

SuperAnnotate is a data annotation tool for engineers and labeling teams. The platform includes a simple communication system, formatted text support, chatbot support and other text-based classes. Labelers can also leverage automatic predictions and data management systems.

"Kili's customer support is best in-class. We solve issues much faster and it has a direct impact on our performance."

Andrea ColonnaHead of Data, Jellysmack

"Great companies like Kili Technology, [...] have already adopted this data-centric AI approach."

Andrew NgData-centric AI Influencer

"Kili is bringing added value in the management of our projects and this is quality."

Gilles HenaffHead of AI, Thales Las France

"Kili enables us to improve our models’ performance and scale our AI projects as fast as our business needs."

Andrea ColonnaHead of Data, Jellysmack

"We are very satisfied with our collaboration with Kili. We saw a performance improvement of our model of 3.5%"

Marie de Léséleuc Director of Analytics and Data Science, Eidos-Montréal

Frequent questions

How do you annotate text?

Annotating text on Kili Technology is simple. Upload your text asset and classify your text, use named entity recognition to identify objects, and label using bounding boxes.

What is a PDF annotation tool?

A PDF annotation tool, like Kili Technology, is a tool that allows the detection and extraction of key information in a PDF document.

What is an annotation tool?

Kili Technology is a global annotation tool that supports the PDF format, but can also help on image, video, audio and text annotation.

What are the applied use cases of text annotation.

Text annotation is an essential step in the training of your ML model, and can be used to build chatbots, bank statement, emails or invoices automatic processes. The goal is to automate existing low-value tasks, and allow workers to be more productive and focus on high-value tasks.

What are best programming tools to be used for text annotation (NLP)?

The best programming tools for NLP are Propagate for Named Entity Recognition (NER), Python for Regex, and Snorkel if you’re looking for open source.

How do I annotate text quickly?

xt, you can use rule based labeling, then manual labeling, then ML based labeling.

What is a good way to annotate text in a contract?

The best way to annotate text in a contract (text or pdf) is to extract important entities from the text with Named Entity Recognition (NER), and link entities that are connected to each other with Named Entity Linking (NEL). To label signatures, stamps, or logos, you can use bounding boxes. bbox for signature & stamps. To do this, use a labeling platform.

What are good annotation tools for chatbot data?

SAP, Google, IBM have solid chatbot building tools. Labeling tools like Kili Technology are also powerful options because of the management of rich text, classification for intent, named entity recognition (NER) and Named Entity Relation (NEL).

What are the most common text annotation formats?

For Named Entity Recognition, the most common format is IOB. For classification and relations, the best formats to use are binary or JSON.

Text & Document Annotation

Natural Language Processing & Machine Learning: An Introduction Counterfactual data augmentation to speed up NLP data labeling Text Recognition for PDFs: How Does It Work?Understanding Named Entity Recognition & Text Classification Intelligent Document Processing: The Beginner's Guide Opinion Classification with Kili Technology and HuggingFace AutoTrain Few-shot Learning methods for Named Entity Recognition An Overview of Deep Learning applied to Natural Language Processing

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