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KEA: Practical Automatic Keyphrase Extraction (1999)

热1 sun9413 添加于 2011-1-14 12:32 | 3573 次阅读 | 1 个评论

作者
Ian H. Witten, Gordon W. Paynter, Eibe Frank, Carl Gutwin
摘要
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 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. We use a large test corpus to evaluate Kea's effectiveness in terms of how many author-assigned keyphrases are correctly identified. The system is simple, robust, and publicly available. INTRODUCTION Keyphrases provide a brief summary of a document's contents. As large document collections such as digital libraries become widespread, the value of such summary information increases. Keywords and keyphrases 1 are particularly useful because they can be interprete...
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- 文献种类:会议
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Kea
附件
Kea.pdf
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