What is best data labeling and why is data labeling needed

The rise of the artificial intelligence industry has led to an increasing demand for data annotation in the AI ​​field. For example, if you want AI to accurately identify pictures, you need to manually label similar pictures in the data set, so that the algorithm and the image can be judged and identified by correlation. And this process requires a lot of pictures to allow AI to learn a model. Data labeling is the most basic work in this process, so what is data labeling? Why do we need data labeling? Let’s introduce it below.

What is Data Labeling?

Data labeling is the process of labeling and detecting data samples, labeling primary data such as pictures and videos that require computer machine learning, allowing the computer to continuously identify the characteristics of these primary data, and finally allowing the computer to identify independently , to provide a large amount of training data for artificial intelligence algorithms to be called by machine learning.

Why is data labeling needed?

The degree of implementation of artificial intelligence AI depends on the data used for learning and training. The quantity and quality of data directly determine the success or failure of AI algorithms. Therefore, when building an Al model, a large amount of training data needs to be continuously inflowed to enrich the future learning of the AI ​​model, that is, supervised learning.

Application scenarios of data annotation:

With the rise of digital image processing and computer platforms, data annotation is gradually integrated into the modern digital field, playing a key role in banking, finance, social media, smart agriculture, digital commerce and other scenarios. The growth of digital content on various business platforms requires the processing of a large amount of user data such as images, videos, and texts, all of which cannot be separated from the basic support of data annotation.

What are the types of data labeling?

There are many types of data labeling, such as frame labeling, classification labeling, area labeling, and point labeling. Basic data annotation types include computer vision, speech engineering, and natural language processing.

1. Computer vision: drawing frame labeling, semantic segmentation, 3D point cloud labeling, key point labeling, line labeling, 2D/3D fusion labeling, target tracking, image classification, etc.

2. Natural language processing: OCR transcription, text information extraction, NLU sentence generalization, part-of-speech tagging, sentiment judgment, intention judgment, machine translation, anaphora resolution, slot filling, etc.

3. Voice engineering: ASR voice transcription, voice emotion judgment, voiceprint recognition and labeling, voice cutting, etc.