Multilingual Text Annotation Services

Adding metadata to a dataset with multilingual text annotation services for high-quality visualization to robots and automated machines.

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text annotation services

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Key Features of Our Text Annotation Services


Annotating Text for Machine Learning

We annotate texts and label metadata for machine learning and artificiaI intelligence (AI) algorithms. Multilingual text annotation is key for making the text recognizable for AI-enabled computer vision. AI and machine learning training based on natural language processing helps machines to understand the human language easily.


Annotating Text with Right Metadata

Annotating texts using natural language processing helps in identifying keywords and annotating the same with descriptive texts. The adding of workable metadata along with your text by annotators without linking another file ensures accuracy for machine learning development.


Annotating Text with High-quality Visualization

We utilize the best tools and techniques to annotate text. Our team of experienced and dedicated professionals can take on and carry out the tasks ensuring the quality level at each stage supplying nothing short of best. We offer top quality text annotation suitable for high-quality visualization within mutually decided time frame.

Techniques in Text Annotation

Text Categorization

Text Categorization

This technique is used frequently in web search engines, document management systems, and other NLP applications. We allow automatic or manual categorization of texts for NLP models. Our ML models can spot topics or themes based on text categorization in a wide range of documents.

Semantic Annotation

This is used for understanding the meaning and context of languages. It can also be used for improving the accuracy of ML algorithms that employ NLP. We help ML models in making more accurate predictions by permitting them to comprehend languages, dialects, and diction in a better way.

Semantic Annotation
Phrase Chunking

Phrase Chunking

Words are grouped into meaningful chunks through annotation and labeling. This technique is used for pre-processing natural language data for ML models. It helps ML models in getting a better understanding of the context and meaning of a sentence.

Entity Linking

This process links entities in a text to a specific item in a knowledge base. It is accomplished through text annotation tools which enrich the model’s understanding of the text and improve the accuracy of text classification models.

Entity Linking

Use Cases for Text Annotation

A wide range of AI use cases can be achieved using data annotation & labeling. We are a leader in data annotation & labeling services for various industries- automobile to retail to e-commerce.


Data annotation & labeling enable 3D object detection which is widely used in robotics for avoiding collisions with dynamic objects like humans, animals, and other characters.


Text Annotation through computer vision algorithms help in detecting traffic signs across highways and lanes.


Embedding annotations & appropriate labels in AI helps in discovering links between genetic codes, powering surgical robots, and optimizing healthcare processes & productivity.

AI in Retail
AI in Retail

Appropriately performed image annotation & data labeling can play a crucial role in automation of AI implementation whilst also helping retailers in enhancing their customers' shopping experience.

Autonomous Flying
Autonomous Flying

AI implementations enabling automated or assisted flight can be made easier and more accessible through image annotation performed at the backend with autonomous flying training data.


IoT sensors and bounding box annotations can provide real-time data for AI algorithms to contribute to agricultural efficiency and yield improvement with real-time insights from their fields.

Frequently Asked Questions

To annotate text, one needs to have an in-depth know-how of the prevailing problem and the data for identifying key features and labeling them. In the context of text classification, it involves looking at sentences, marking them, and putting them in predefined categories – online review labeling as positive or negative, news clippings as fake or real.

The key guidelines are a set of rules and suggestions which act as a reference point for annotators. The guidelines may vary from one team to another. Given below is an example which your team can follow during text annotation.

1. Curating guidelines for annotation
2. Selecting a labeling tool
3. Defining an annotation process
4. Reviewing and quality control

Manual annotation has an edge over automatic annotation as it helps in grasping the subtleties and intricacies of text ensuring precision. It’s time-consuming and costly as it involves human work. On the other hand, automatic annotation is much more effective as it can be done quickly and on a vast scale on more difficult tasks. The quality of annotation may be low due to manual annotation. A hybrid approach like the one we follow at Anolytics ensures both precision and speed.

Through an accurate training dataset, an AI model can learn and grow to interpret human language in a consistent manner. By offering training data that’s complete in every way, machine learning algorithms can assist in developing self-predicting AI. In several instances, AI and ML developers have a preference for human annotators for highlighting texts in different dialects, sentiments, meaning, and use for maintaining and enhancing accuracy.

Interested in Working with Us?

In today's tech-driven world, a career in Artificial Intelligence (AI) can be highly rewarding. Join our team of Annotation Specialist, and be a part of the company that creates high-quality training datasets.

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USA Office USA Office

16 Horseshoe Ln, Levittown, NY 11756, United States

India Office India Office

A-83, Sector-2, Noida, Uttar Pradesh 201301

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