Text Annotation

  • One of the most common types of media is text, which makes up the languages we use to communicate. This process is so commonly used that text annotation needs to be done with accuracy and comprehensiveness. 

 

  • With machine learning (ML), machines are taught how to read, understand, analyze, and produce text in a valuable way for technological interactions with humans. As per the statistics of AI and Machine Learning survey, the majority of companies gave a report that text data is used as part of their AI solutions. These generally lead to cost-savings and revenue-generating implications, for text-based solutions across all industries which is humongous. 

 

  • As machines improve their ability to interpret human language, the importance of training using high-quality text data becomes increasingly undeniable. In all cases, preparing accurate training data must begin with accurate, comprehensive text annotation. 

 

  • Algorithms often use large amounts of annotated data to train AI models, which is part of a larger data labeling workflow. During the annotation process it is used to markup characteristics of a dataset which are called meta-data tags. With text annotation, that data includes tags that highlight criteria such as keywords, phrases, or sentences. In certain applications, text annotation can also include tagging various sentiments in text, such as “angry” or “sarcastic” to teach the machine how to identify human intent or emotion behind words. 

 

  • The annotated data, known as training data, is what the machine processes. The goal is to help the machine understand the natural language of humans. This procedure is known as natural language processing, or NLP. 

 

  • These tags must be accurate and comprehensive. Poorly done text annotations will lead a machine to exhibit grammatical errors or issues with clarity or context.  

 

  • A machine will learn to communicate well-organized enough in natural language after being trained on accurately annotated text data. It can carry out the more repetitive and important tasks humans would otherwise do. This frees up time, money, and resources in an organization to enable focus on more strategic goals. 

 

  • The applications that are used on a daily basis include mostly natural language-based AI systems which are endless: smart chatbots, e-commerce, voice assistants, machine translators, more better search engines. Most business houses seek out human annotators to label text data. Human annotators are especially valuable in analyzing affectionate data or data which contains slang/indecent words which Machine may not be able to comprehend. 

 

  • Text data in English language or various other foreign languages   may require annotators to have relevant knowledge and skills. This may pose a constraint when you’re scaling your data annotation effort.  

 

 

  • Text data can also be extracted from images, audio, and video files. If such needs occur, you’d need your annotation platform or service provider to be able to handle the transcription task from these non-text data. This is also something that you should take into consideration when choosing your annotation solutions. 

 

  • By annotating a text, we will understand what the goal of the text is after we have read it. As we start annotating, we should carefully observe the author’s main points that include the grammar or the perspective of the text, area of focus and our own thoughts to summarize the text. 

 

Conclusion 

To summarize text annotation, we need to enhance the reader’s understanding of, recall off and our reaction to the text matter. Annotation usually involves highlighting or underlining the key pieces of information and making notes of them.