![]() ![]() ![]() ![]() Although the terminology is different, the function is the same: characterization of the topic discussed in a document. ‘Key phrases’, ‘key terms’, ‘key segments’ and ‘keywords’ for instance are the terminology used for defining the most relevant information contained in a document. The realm of keyword extraction is complex and one can easily be overcome with confusing terminology. But it also works the other way around keyword extraction will enable easy search and retrieval of content on topics, keywords, (learning) objectives and readability levels. You can use a keyword extractor to pull out single words (keywords) or groups of two or more words that create a phrase (key phrases). Keyword extraction may be the key to finding relevant keywords within massive sets of data (like books, articles, papers, or journals) without having to actually read the whole content. Keyword extraction is about automatically finding what’s relevant in a large set of data. This way, you can easily identify which parts of the available data cover the subjects you are looking for while saving your teams many hours of manual processing. Keyword extraction technique will sift through the whole set of data in minutes and obtain the words and phrases that best describe each subject. Imagine you need to analyze dozens of textbooks as part of a curriculum. It helps summarize the content of a text and recognize the main topics. Keyword extraction (also known as keyword analysis) is a technique that automatically identifies and extracts the words that best describe the subject of a document. ![]()
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