Text Data Annotation for NLP

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Despite the massive shift towards digitization, some of the most complex layers of data are still stored in the form of text on paper or official documents. With the plethora of publicly available information, there comes the challenge of managing unstructured, raw data and making it understandable for machines. Unlike images or videos, texts are more complicated. Let’s take a sample sentence: “They nailed it!”. Humans are expected to understand it as applause, encouragement, or appreciation, while the traditional Natural Language Processing (NLP) model is likely to perceive the surface-level representation of the word, missing out on the intended meaning. Namely, it may associate the word nail with hammer nailing. Accurate text annotations help models better grasp the data provided, resulting in an error-free interpretation of the text. We will use this opportunity to build up your knowledge of this integral type of data annotation by covering the fundamentals as listed below:

  • What is text annotation?
  • Why is it important?
  • How is text annotated: NLP text annotation
  • Text annotation for OCR
  • Types of text annotation
  • Use cases of text annotation
  • Final thoughts
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