KEA: practical automatic keyphrase extraction
Proceedings of the fourth ACM conference on Digital libraries
The C-value/NC-value Method of Automatic Recognition for Multi-Word Terms
ECDL '98 Proceedings of the Second European Conference on Research and Advanced Technology for Digital Libraries
Measuring inter-indexer consistency using a thesaurus
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Domain-independent automatic keyphrase indexing with small training sets
Journal of the American Society for Information Science and Technology
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In this paper, we report on indexing performance by a state-of-the-art keyphrase indexer, Maui, when paired with a text extraction procedure called text denoising. Text denoising is a method that extracts the denoised text, comprising the content-rich sentences, from full texts. The performance of the keyphrase indexer is demonstrated on three standard corpora collected from three domains, namely food and agriculture, high energy physics, and biomedical science. Maui is trained using the full texts and denoised texts. The indexer, using its trained models, then extracts keyphrases from test sets comprising full texts, and their denoised and noise parts (i.e., the part of texts that remains after denoising). Experimental findings show that against a gold standard, the denoised-text-trained indexer indexing full texts, performs either better than or as good as its benchmark performance produced by a full-text-trained indexer indexing full texts.