In this growing digital world, businesses should always look for the massive data of the end users on their different platforms. This differentiates them from their competitors. The most common data that businesses can use is in text form. From social media remarks to products & services reviews, understanding all such data in massive text quantities is essential.
Unfortunately, the plenty of information in text form becomes a great challenge to deal with—the unstructured and machine unreadable data. Unlike data in other states, the text is more perplexing. So it becomes important to make the data structured so that machines can easily understand.
Here comes the automatic text annotation tool. Algorithmsuse a huge amount of annotated data to train AI models accurately. In between the annotating process, a metadata tag is used to define the characteristics of a dataset. The most important factor to build high-precision models is quality.
Text Annotation
Text Annotation consists of associating labels to digital text data and its content. Text annotation converts a text into a dataset which is used in machine learning and deep learning models for different types ofcomputer vision applications.
In simple terms, text annotation is appending to the text with different standards based on the requirement and the use case. Annotation can be phrases, words, sentences, etc. The annotation also assigns different labels like - names, intention, sentiment, etc.
Types of Text Annotation
Text annotation categories in multiple types: -
Sentiment Annotation
Sentiment annotation is the annotation of corresponding sentences. Sometimes, it is difficult to determine the emotion of the sentences over a handwritten text. But sentiment analysis can annotate with such kind of data.
Entity Annotation
Entity annotation is the key phrases, entities, or parts of speech. Entity annotation helps drive attention to the important details of long text. This annotation also helps to drive attention to some of the crucial details from the long text. This annotation also helps to prepare the data models that draw out different kinds of entities from the huge amounts of data.
Conclusion
Text annotation plays an important role, as organizations require a large amount of data for training on different machine learning models.
Well annotated data improves the quality of data to enhance the accuracy of AI models. For a proper AI model, you need to attain higher clarity, The first step is to prepare well-annotated data. This demands the use of text annotation in the case of NLP (Natural Language Processing).
UBIAI’s text annotation tool is one of the best natural language processing tools that can train the custom NLP models faster and transform the unstructured data into intelligence.
