Text Annotation – What is it and Why is it Important?

In an age where chatbots, email filters, and multilingual translators are all the rage, creating intelligent AIs as the next breakthrough technology often requires nothing more than an idea.

Text annotation is important in developing NLP-powered software because it helps teach algorithms to read and comprehend written text.

Still, have questions about how text annotation for Machine Learning works? Consider visiting a website with built-in chatbots at 3 a.m., where you can type in questions and get answers in the blink of an eye. 

Of course, you cannot expect someone to respond at such an odd hour. However, when the chatbots receive a query, they quickly retrieve responses from the training data.

Machine Learning Requires Accurate Text Annotation

As appealing as the concept is, creating similar resources can require significant time, professional experience, and expert-level intellect. This is where Subul shines as a trustworthy text annotation company, focusing on perfectly labeling the collected data.

With Subul on board, you can stop worrying about your machine learning setups’ perceptual abilities because the AI training data on offer is ready to interpret responses, semantics, and sentiments.

Looking for more? Here are some additional advantages of partnering with Subul as your Text Annotation outsourcing partner.

• A  Goal-Oriented Strategy
• Focus on context and communication clarity. 
• Capability to train machines with linguistic elements
• Comprehensive search engine labeling
• Offerings that can be scaled up
• Machine translation in multiple languages

Text Annotation Services by Subul – Get Goal-Specific Text Labeling Services

We offer cognitive text labeling services via our patented text labeling tool, designed to help organizations find critical information in unstructured text. In addition, annotating available text aids machines in understanding human language. 

We are well-equipped to handle text labeling projects of any size due to our extensive experience in natural language and linguistics. Our skilled team can work on various text labeling solutions, such as named entity recognition, intent analysis, sentiment analysis, document annotation, etc.

Choose one that meets your needs and leave the heavy lifting to Subul. Here are some examples of annotated text.

Text Categorization

The most basic approach to text annotation focuses on categorizing text based on content type, intent, sentiment, and subject. After categorizing the datasets, they are fed into the system as part of a predefined segment that machines can use to generate a response.

Linguistic Annotation

This type of textual dataset labeling, originally known as corpus annotation, focuses on the language details of audio and texts, phonetic annotation, bits of semantic annotation, POS tagging, and so on. This approach is useful for training machine translation models.

Entity Annotation

When it comes to Chatbot training, this labeling method is critical. Before feeding the data into the system, the emphasis is on extracting, locating, and tagging entities. Named entities, key phrases, and POS, such as adjectives, adverbs, and more, as with any Chatbot-powered interface, become the focal point.

Entity Linking

While annotators retrieve entities from larger data repositories, they must be linked to form meaningful datasets.

This is one of the few tools that allow for creating entire knowledge databases through disambiguation and quick routing from a chat interface — for example, URL recognition and a link to a website.