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Artificial intelligence (AI) and machine learning technologies are frequently used in conjunction with optical character recognition (OCR) software, which can scan text and images and convert that data into digital format.
The benefit of OCR technology that is powered by machine learning and AI technology is that it can process large volumes of content, improving along the way. Text data annotation makes this self-evolution possible.
With text data annotation, text, keywords, phrases, sentences and images are tagged or labeled to indicate that the machine learning and AI algorithm should make note of this information. It’s a bit like saying, “This information is important. Please make note of it, learn from it and apply that to future projects.”
Text data annotation is an important part of algorithm development. Machine learning processes the data and AI tells the algorithm how to use or apply that information.
Why Outsource Text Data Annotation Projects?
To be effective, text data annotation must be thorough. This takes time and insight into how the data is being used. Experienced text and data annotation specialists can add labels for the following functions (among others):
- Guide business decisions;
- Gather demographic information;
- Gather psychographic information;
- Drive website traffic; and
- Influence buying decisions.
These are just a few of the functions you can achieve when using text and data annotation to refine an AI and machine learning algorithm.
Additionally, data annotation technicians can add labels that take the place of sentiment analysis. You might label terms like “wistful” or “happy” when developing a messaging platform.
By outsourcing a text data annotation project, businesses can maximize their chances of getting an optimal result in an expedient, cost-effective manner.
Advantages of Outsourcing Text Data Annotation
Natural language processing (NLP) and text data annotation requires a lot of insight into artificial intelligence, machine learning and how these technologies work. Without this insight, it’s virtually impossible to know which phrases and terms to label. Label the wrong content and your NLP project is ruined by skewed results.
Text data annotation requires a lot of insight into the algorithms that will be created or updated. If the algorithm is being created for a text messaging platform, sentiment-related terms will be the focus.
On the other hand, keywords and key phrases might be your emphasis if you are a SEO firm seeking to create an algorithm that will be used to evaluate client websites.
A service provider who specializes in text data annotation will be well-positioned to label the content that will meet your needs and bring optimal results. Labeling the wrong terms can skew an algorithm, rendering it useless.
Conversely, labeling the right terms will enhance an algorithm, making it more powerful over time.
Outsourcing Text Data Annotation as Part of a Data Capture Strategy
A text data annotation approach is a critical part of any data capture strategy since it allows for the creation of better algorithms. At iTech, we specialize in data capture and other data services. We invite you to reach out to discuss your needs.
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