Kea

Keyphrases provide semantic metadata that summarize and characterize documents.Kea is an algorithm for automatically extracting keyphrases from text.We use a large test corpus to evaluate its effectiveness in terms of how many author-assigned keyphrases are correctly identified.The system is simple, robust, and publicly available.Kea identifies candidate keyphrases using lexical methods, calculates feature values for each candidate, and uses a machine-learning algorithm to predict which candidates are good keyphrases.The machine learning scheme first builds a prediction model using training documents with known keyphrases, and then uses the model to find keyphrases in new documents.Keyphrases are useful because they briefly summarize a document's content.As large document collections such as digital libraries become widespread, the value of such summary information increases.Keywords and keyphrases are particularly useful because they can be interpreted individually and independently of each other.They can be used in information retrieval systems as descriptions of the documents returned by a query, as the basis for search indexes, as a way of browsing a collection, and as a document clustering technique (e.g.[2], [3], [4]).Keyphrases are usually chosen manually.In many academic contexts, authors assign keyphrases to documents they have written.Professional indexers often choose phrases from a "controlled vocabulary" that is predefined for the domain at hand.However, the great majority of documents come without keyphrases, and assigning them manually is a tedious process that requires knowledge of the subject matter.Automatic extraction techniques are potentially of great benefit. THE KEA ALGORITHMKea is an algorithm for automatically extracting keyphrases from text.The algorithm has two stages: