Text annotation work is the work of corresponding labels or tags to specific content in the text. Its main purpose is to provide additional information to the text for deeper analysis and processing, especially in the field of artificial intelligence.

#Text annotation is crucial for supervised machine learning tasks in artificial intelligence applications. It is used to train AI models to help more accurately understand natural language text information and improve the performance of tasks such as text classification, sentiment analysis, and language translation. Through text annotation, we can teach AI models to recognize entities in text, understand context, and make accurate predictions when new similar data appears.

This article mainly recommends some better open source text annotation tools.

1.Label Studio

https://github.com/HumanSignal/label-studio

##Label Studio is an open source Data annotation tool, capable of processing multiple data types and supporting export to multiple model formats. It is widely used to prepare raw data or improve existing training data to improve the accuracy of machine learning models.

2.Doccano

https://github.com/doccano/doccano

Doccano is an open source text annotation tool that provides functions for text classification, sequence labeling, and sequence tasks. It supports text annotation team collaboration, multi-language, mobile apps, emojis, dark themes, and REST-style APIs. Can be installed using Docker and Docker Compose.

3.Universal Data Tool

https://github.com/UniversalDataTool/universal-data-tool

Universal Data Tool is a versatile application for editing and annotating various types of data, including images, text, audio, and documents. It supports a wide range of data types and provides real-time collaboration, easy-to-use GUI, creation of training courses for text annotators, and more. The tool is available on the web or as a desktop application and supports data download and upload in CSV or JSON format.

4.YEDDA

https://github.com/jiesutd/YEDDA

YEDDA is a text annotation tool that can be used in various languages, symbols and emoticons. It supports using shortcuts, commanding the model, and exporting annotation text as sequence text. Supports functions such as intelligent recommendations and administrator analysis.

YEDDA is compatible with all major operating systems, including Windows, Linux and MacOS.

5.Argilla

https://github.com/argilla-io/argilla

Argilla is a platform for artificial intelligence engineers An open source data collaboration platform with domain experts to provide high-quality and efficient data output.

It helps control data quality and improve AI output quality, and improves efficiency by enabling rapid iteration of data and models. Argilla also provides data management and model training tools.

6.KernAI Refinery

https://github.com/code-kern-ai/refinery

Refinery is an open source platform from KernAI designed for data scientists working with natural language data. It provides functions such as semi-automated data annotation, data subset quality assessment, and centralized data monitoring, aiming to improve manual labeling efficiency.

The tool leverages technologies such as Hugging Face and spaCy to build pre-built language models and integrates with other labeling tools for flexible data processing.

Features:

• (Semi-)automated labeling workflow for NLP tasks
• Manual and programmatic classification and span labeling
• Support with State-of-the-art library and framework integration
• Create and manage lookup tables/knowledge bases
• Neural search-based similar record and outlier retrieval
• Sliceable tag sessions
• Multiple tag tasks per project
• Rich automation library
• Extensive data management and monitoring
• Integration with Hugging Face for automated creation of embeds
• JSON-based data model for data upload/download
• Project Metrics Overview
• Access and extend data via Python SDK
• In-place attribute modification
• Team collaboration in the hosted version
• Role-based access and minimized tag view for multiple users
• Integrated group tag workflow
• Automatically Calculate agreement between annotators

7.Recogito.js

##https://github.com/recogito/recogito-js

ApplitoJS is a JavaScript library for text annotation, used to add text annotation functionality to web pages or build custom text annotation programs. It can be installed via npm or downloading the latest version.

8.Label Sleuth

https://github.com/label-sleuth/label-sleuth

Label Sleuth is an open source, no-code system for text labeling and classification. It enables experts in fields such as doctors, lawyers, and psychologists to build custom NLP models without the cooperation of NLP experts.

Usually NLP model creation requires domain and machine learning expertise. Label Sleuth bypasses the requirement for NLP expertise with intuitive text annotation and AI model building. While users are labeling data, machine learning models are trained in the background, making predictions and suggesting what to label next.

As a no-code system, it requires no machine learning knowledge and allows rapid model development, from task definition to completed model in just a few hours.

9.Markup

https://github.com/samueldobbie/markup

Markup is an online annotation tool that can be used to convert unstructured documents into structured formats for NLP and ML tasks, such as entity recognition. Simultaneous learning as you annotate to predict and recommend more complex annotations, and also provides integrated access to common and custom ontologies for concept mapping.

Features:

• Predictive annotation: Markup’s machine learning-driven predictive annotation function can recommend more complex annotations as you work, making the annotation process more efficient. .
• Integrated ontology access tags: Provides integrated access to a wide range of common ontologies (e.g. UMLS, SNOMED-CT, ICD-10), as well as the ability to upload custom ontologies for concept mapping.
• Predictive ontology mapping: Markup’s predictive ontology mapping feature uses machine learning to recommend appropriate mappings to standard and custom terms based on the text you are annotating.
• User-Friendly Interface: Whether you are a technical expert or a beginner, Markup's user-friendly interface makes it easy for anyone to start annotating documents with minimal setup.

10.Potato

https://github.com/davidjurgens/potato

Potato is a web-based text annotation tool that supports quick setup and deployment of various text annotation tasks. Can run as a web server, driven by a single configuration file, requiring no startup coding. But Potato is easy to customize, and usually does not require additional web design to adjust the user interface for text annotators.

Key Features:

• Easy to set up and customize
• Extensive built-in schemas and templates
• Supports multiple data types
• Support multi-tasking settings
• Improve annotation efficiency with features such as keyboard shortcuts, dynamic highlighting, and label tooltips
• Get a better understanding of annotator functions, such as before and after filtering Questions
• Quality control features like attention test, qualification test and built-in time check