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Keyphrase Extraction Algorithm

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.

Digital Library Group and Machine Learning Group

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