Definition

Data annotation is simply the process of labeling information for machine use. This is especially useful for supervised machine learning (ML), where a system relies on a set of labeled data to process, understand, and learn  input patterns to achieve the desired result.

In ML, data annotation occurs before information is entered into the  system. This process can be compared to teaching children using flashcards. A card with  an apple and the word "apple" will tell  children what an apple looks like and what the word is spelled. In this example, the word "apple" is a label.

Data annotations are an integral part of supervised machine learning. Without it, the machine cannot parse the input data correctly  to produce the desired output. In this section, we will look at the different types of data annotations and some important use cases. To learn more about data annotation, you can also check out Data Annotation Guide: Everything Beginners Need to Know.

Data Annotation Types in Machine Learning

Data may be annotated in a variety of ways for machine use, including:

1.Semantic Annotation

This method involves naming various concepts in text, such as "things," "people," and "names." Semantic annotations are used to train chatbots and improve the relevance of search engine results. Watch this video for more details.

2. Image and Video Annotation

Image and video tagging allows machines to understand image and video content. Often, developers use bounding boxes to tell the computer what to focus on so that a specific object can be identified. Image and video annotations are commonly applied to self-driving vehicles and e-commerce product listings.

3. Text Classification or Categorization

This method refers to the process of extracting common tags from unstructured text. Generic tags come from a predefined set of  categories. Categorizing or classifying text makes it easy for users to search for information and navigate your website or  application.

Example of using data annotations

Data annotations are useful when:

1. Improving the Quality of Search Engine Results for Multiple User Types

Search engines must provide users with comprehensive information. Their algorithm has to process a large set of labeled data to provide the correct answer. Take Microsoft Bing, for example. Because they serve multiple markets, vendors need to ensure that the results their search engines provide are consistent with users' culture, line of business, etc.

2. Improve your local search score

While search engines serve users worldwide, vendors must also provide localized results to users. Data annotators can help with this by tagging information, images, and other content based on location information.

3. Improve the relevance of your content on social media

Like search engines, social media platforms must also  provide users with personalized content recommendations. Data annotations can help developers classify and classify content based on relevance. For example, a classification of content that users may consume or value based on their browsing habits and that they consider relevant based on where they live or work.

Data annotation is time consuming and tedious. Fortunately, artificial intelligence (AI) systems can be used to automate this process.

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