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KEA: Practical Automatic Keyphrase Extraction (1999)
| Content Provider | CiteSeerX |
|---|---|
| Author | Witten, Ian H. Paynter, Gordon W. Frank, Eibe Gutwin, Carl Nevill-Manning, Craig G. |
| Description | Keyphrases provide semantic metadata that summarize and characterize documents. This paper describes Kea, an algorithm for automatically extracting keyphrases from text. Kea identifies candidate keyphrases using lexical methods, calculates feature values for each candidate, and uses a machinelearning 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. We use a large test corpus to evaluate Keas effectiveness in terms of how many author-assigned keyphrases are correctly identified. The system is simple, robust, and publicly available. |
| File Format | |
| Language | English |
| Publisher | ACM Press |
| Publisher Date | 1999-01-01 |
| Publisher Institution | PROCEEDINGS OF DIGITAL LIBRARIES 99 (DL'99 |
| Access Restriction | Open |
| Subject Keyword | Known Keyphrases Characterize Document Semantic Metadata Many Author-assigned Keyphrases Prediction Model Practical Automatic Keyphrase Extraction Kea Effectiveness Kea Identifies Large Test Corpus Good Keyphrases New Document Feature Value Lexical Method |
| Content Type | Text |
| Resource Type | Article |
