What is Text Annotation in Machine Learning ? Types of Text Annotation.

Text annotation is the machine learning process of assigning meaning to blocks of text: whether they are short phrases, longer sentences or full paragraphs. This is done by providing AI models with additional information in the form of definitions, meaning and intent to supplement the text as written.

The texts are annotated with metadata and highlighted with specific colors and shades by highly skilled annotators making sure each text is read carefully in order to train the NLP machine learning algorithm accurately.

Why is Text Annotation Important?

The importance of text annotation in NLP is due to the diversity of human languages. Machines, no matter how intelligent they become, still have a lot to learn about context and deeper meaning. It's an annotation that tells them what they need to know.

Chatbots are among the most well-known implementations of natural language processing today, and there are hundreds of examples of bots that have gone wrong. Failures of chatbots can be amusing. Poorly trained chatbots, particularly those in customer support, can harm a company's reputation, user experience, and, ultimately, client loyalty.

While tools exist for automatic text annotation, some of the highest quality annotations come from human annotators. From being able to understand complex sentiments to expertly annotating highly technical subjects, human annotators produce superior results.

What are the types of Text Annotation?

Datasets with text annotations usually contain highlighted or underlined key pieces of text, along with notes around its margins. By annotating text, you can ensure that the target reader, in this case a computer, can better understand key elements of the data.

The process of annotating text involves any action that deliberately interacts with digital contextual data. So for those who need to build text datasets, here’s an introduction to different types of text annotation methods:

Named Entity Recognition

Named Entity Recognition is the act of locating and labeling mentions of named entities within a piece of text data.This includes identification of entities in a paragraph(like a person, organization, date, location, time, etc.), and further classifying them into categories according to the need.

Part-of-speech tagging

Part-of-speech tagging is the task that involves marking up words in a sentence as nouns, verbs, adjectives, adverbs, and other descriptors.This is where the functional elements of speech within the text data is annotated.

Summarization

Summarization is the task that includes text shortening by identifying the important parts and creating a summary. It involves Creating a brief description that includes the most important and relevant information contained in the text.

Sentiment analysis

Sentiment analysis is the task that implies a broad range of subjective analysis to identify positive or negative feelings in a sentence, the sentiment of a customer review, judging mood via written text or voice analysis, and other similar tasks.

Text classification

Text classification is the task that involves assigning tags/categories to text according to the content. Text classifiers can be used to structure, organize, and categorize any text. Placing text into organized groups and labeling it, based on features of interest.This is often used for labelling topics, detecting spam, analyzing intent and emotional sentiment.

Keyphrase Tagging

This is a procedure for locating keyphrases or keywords in text. Also known as keyword extraction, this is often used to improve search-related functions for databases, ecommerce platforms, self-serve help sections of websites and so on.

TagX Text Annotation

TagX offers Text annotation services for machine learning. Having a diverse pool of accredited professionals, access to the most advanced tools, cutting-edge technologies, and proven operational techniques, we constantly strive to improve the quality of our client’s AI algorithm predictions.

With the perfect blend of experience and skills, our outsourced data annotation services consistently deliver structured, highest-quality, and large volumes of data streams within the desired time and budget. As one of the leading providers of data labeling services, we have worked with clients across different industry verticals such as Satellite Imagery, Insurance, Logistics, Retail, and more.