text mining, scientific digital libraries, subject metadata, keyphrase
annotation, keyphrase indexing, Wikipedia, genetic algorithms
Topical annotation of documents with keyphrases is a proven method
for revealing the subject of scientific and research documents. However, scientific documents that are manually annotated with keyphrases are in the minority. This paper describes a machine learning-based automatic keyphrase annotation method for scientific documents, which utilizes Wikipedia as a thesaurus
for candidate selection from documents’ content and deploys genetic algorithms
to learn a model for ranking and filtering the most probable keyphrases. Reported experimental results show that the performance of our method, evaluated
in terms of inter-consistency with human annotators, is on a par with that
achieved by humans and outperforms rival supervised methods.