From search engines and sentiment analysis to virtual assistants and chatbots, there are numerous areas of research within machine learning that require text annotation tools and services.
In the AI research and development industries, annotated data is gold. Large quantities of high-quality annotated data is a goldmine. On the other hand, sometimes finding or creating this data can be an expensive and arduous task for your team. Fortunately, there are a variety of text annotation tools and services available that can provide you with the data you need. Some of these services include entity extraction, part-of-speech tagging, and sentiment analysis.
What are the Best Text Annotation Tools and Services?
Read on below to find out which text annotation service or tool is best for your project.
1. Tagtog
Based in Poland, Tagtog is a text annotation tool that can be used to annotate text both automatically or manually. Tagtog supports native PDF annotation and includes pre-trained NER models for automatic text annotation. On top of the Tagtog tool, the company also has a network of expert workers from various fields that can annotate specialized texts.
2. LightTag
The LightTag text annotation tool is a platform for annotators and companies to label their text data in house. While the starter package is free, each package level rises in cost and has a monthly limited amount of annotations, starting from 1000 annotations a month.
3. Lionbridge AI
With a specialization in linguistics, Lionbridge has a community of 1 million annotators fluent in over 300 languages. Some of our text annotation services include text extraction, sentiment classification, entity annotation, named entity recognition, and linguistic component analysis. Furthermore, Lionbridge also offers a custom data annotation software that your team can license and use for a variety of text annotation projects.
4. Scale
Based in San Francisco, Scale is a provider of computer vision and NLP data annotation services. Through a combination of human work and Scale’s platform, the company provides the following text annotation services: OCR transcription, text categorization, and comparison.
5. KConnect
One problem many AI researchers and developers face is getting access to AI training data for highly specialized fields. The team at KConnect seeks to help annotators quickly and efficiently classify and annotate medical data. Specifically, KConnect provides semantic annotation, text analysis, and semantic search services for medical information.
6. Clickworker
Based in the United States and Germany, Clickworker is a crowdsourcing company that has a huge workforce able to perform a variety of tasks. Some of their services include sentiment analysis and categorization.
7. ParallelDots Text Annotation APIs
ParallelDots is a provider of numerous text annotation tools and APIs. Some of their solutions include sentiment analysis, emotion analysis, keyword extractors, and named entity recognition.
8. Appen
With a huge source of crowdworkers from various countries, Appen is a provider of numerous forms of AI training data. For instance, some of their text annotation services include sentiment annotation, intent annotation, and named entity annotation.
9. Dandelion API
Based in Italy, Dandelion API provides a variety of automatic text annotation tools. While they are a relatively new startup company, their tools can be used for entity extraction, sentiment analysis, and text and content classification.
10. Dataturks Text Annotation Tools
With an in-house API for data annotation and thousands of partnered outsourcing companies, Dataturks provides various image annotation and text annotation tools. Specifically, some of their text labeling capacities include text classification, named entity recognition, and part-of-speech labeling.
Top comments (1)
A very informative post, indeed! Text annotation plays a crucial role in enhancing the accuracy and performance of various AI applications, from sentiment analysis to virtual assistants. Each tool/service seems to have its unique features and advantages, catering to different needs and requirements. Since the past year, there has been a surge in the growth of such annotation tools because of their reliability and scalability. For some of my projects, I have used NLP Lab which provides a no-code environment for document labeling, over a variety of data types - from plain text to PDFs. I chose to use this also because it is free :D