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DIFFERENCE TYPES OF DATA TAGGING SERVICES

There are different types of Data Labeling. Each type will suit a different use case. Here are some common labeling styles used for machine learning projects.

Semantic Labeling

Semantic labeling is the task of annotating different concepts in a text. Examples of semantic labeling are people, objects, or company names. Machine learning models use semantically annotated data to learn how to classify new concepts in new text. This can help improve search relevance and train chatbots.

DIFFERENCE TYPES OF DATA TAGGING SERVICES
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Label images and videos

Image labeling takes many different forms, from framing on images, to semantic segmentation. In image labeling, each pixel in the image is assigned a meaning. This label often helps the machine learning model recognize the annotated area as a distinct object. This type of data is often the basis for image recognition models. It can identify and block sensitive content. And instruct autonomous vehicles or perform facial recognition tasks. Image labeling, video labeling often involves adding frames or key points to content. This can be done on a frame-by-frame basis. These frames are then stitched together to help track the movement of the labeled object within the video itself using the video annotation tool. This type of data plays an important role in the development of computer vision models. It supports tasks such as tracking and object localization.

Text classification

Text classification and content classification refer to the task of evaluating predefined categories for documents. For example, you can tag sentences or paragraphs in a document by topic, or organize articles by topic such as national, international, sports, or entertainment.

Entity Labeling

Entity labeling is the process of labeling unstructured sentences with machine-readable information. In entity annotation, there are countless processes that can be sub-classed to create an understanding of the language. For example:

  • Named Entity Recognition (NER): this job deals with the classification of named objects contained in textual content. These entities are labeled based on predefined categories like people, organizations, and places. Named entity recognition models add semantic knowledge to your content, making it easy for individuals and systems to quickly identify and understand the subject matter of any given text.
  • Entity linking: This is the process of annotating the relationship between two pieces of text.

In addition to the above types of data labeling, there are many other types of data labeling. In this article, BPO.MP only provides information about the above types of labeling for your further reference.  

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