Open Source Text Processing Project: KEA

Deep Learning Specialization on Coursera

KEA: Keyphrase Extraction Algorithm

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Keywords and keyphrases (multi-word units) are widely used in large document collections. They describe the content of single documents and provide a kind of semantic metadata that is useful for a wide variety of purposes. The task of assigning keyphrases to a document is called keyphrase indexing. For example, academic papers are often accompanied by a set of keyphrases freely chosen by the author. In libraries professional indexers select keyphrases from a controlled vocabulary (also called Subject Headings) according to defined cataloguing rules. On the Internet, digital libraries, or any depositories of data (flickr,, blog articles etc.) also use keyphrases (or here called content tags or content labels) to organize and provide a thematic access to their data.

KEA is an algorithm for extracting keyphrases from text documents. It can be either used for free indexing or for indexing with a controlled vocabulary.

KEA is implemented in Java and is platform independent. It is an open-source software distributed under the GNU General Public License.

In real life, the Kea is one of New Zealand’s native parrots, famed for theft, destroying cars and cameras, forming street gangs, pecking sheep to death for their delicious kidney fat, and other cutesy antics.

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