![]() ![]() These tags must be accurate and comprehensive. This procedure, combined with data pre-processing and annotation, is known as natural language processing, or NLP. The goal? Help the machine understand the natural language of humans. The annotated data, known as training data, is what the machine processes. In certain applications, text annotation can also include tagging various sentiments in text, such as “angry” or “sarcastic” to teach the machine how to recognize human intent or emotion behind words. With text annotation, that data includes tags that highlight criteria such as keywords, phrases, or sentences. During the annotation process, a metadata tag is used to mark up characteristics of a dataset. In all cases, preparing accurate training data must begin with accurate, comprehensive text annotation.Īlgorithms use large amounts of annotated data to train AI models, which is part of a larger data labeling workflow. Understandably so, as the cost-savings and revenue-generating implications of text-based solutions across all industries are enormous.Īs machines improve their ability to interpret human language, the importance of training using high-quality text data becomes increasingly indisputable. Per the 2020 State of AI and Machine Learning report, 70% of companies reported that text is a type of data they use as part of their AI solutions. With machine learning (ML), machines are taught how to read, understand, analyze, and produce text in a valuable way for technological interactions with humans. Because it is so commonly used, text annotation needs to be done with accuracy and comprehensiveness. ![]() One of the most common types of media is text, which makes up the languages we use to communicate. Everything You Need to Know About Text Annotation with Yao XuĮvery day, we interact with different media (such as text, audio, images, and video), relying on our brain to process what media we are seeing and make meaning out of it to influence what we do. ![]()
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