Extracting Keywords From Text Using Natural Language Processing

This post was originally published on DZone (IoT)

If your work involves data analysis, SEO optimization, or any role that requires sifting through digital content, understanding how to effectively extract keywords is essential. This skill streamlines data management and accessibility, representing a significant advancement in data processing and analysis methodologies.

Right at the center of this ability is Natural Language Processing. This cutting-edge technology empowers computers to understand human language, effectively narrowing the divide between digital data management and actual human interaction. Within this domain, the spaCy Python library stands out for its robust language processing features. Known for its efficiency and ease of use, spaCy empowers professionals across various fields to enhance their text analysis practices, improving their workflows and outcomes.

With this article, I would like to help you broaden your understanding of NLP and show how spaCy can be your powerful ally in effective keyword extraction. By exploring these technologies, you will be equipped with the knowledge to leverage their potential, boosting your ability to extract valuable information from text.

Understanding Natural Language Processing

NLP is an essential field within the broader AI discipline that bridges human communication and computer understanding. It’s the technology behind the computer’s ability to interpret, understand, and derive meaning from human

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