Towards multi-granularity multi-facet e-book retrieval
Proceedings of the 16th international conference on World Wide Web
KP-Miner: A keyphrase extraction system for English and Arabic documents
Information Systems
An Automatic Online News Topic Keyphrase Extraction System
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Clustering to find exemplar terms for keyphrase extraction
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Large-scale cross-media retrieval of WikipediaMM images with textual and visual query expansion
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
SemanticRank: ranking keywords and sentences using semantic graphs
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
MFSRank: an unsupervised method to extract keyphrases using semantic information
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Enhancing biomedical concept extraction using semantic relationship weights
International Journal of Data Mining and Bioinformatics
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Keyphrases play a key role in text indexing, summarization and categorization. However, most of the existing keyphrase extraction approaches require human-labeled training sets. In this paper, we propose an automatic keyphrase extraction algorithm, which can be used in both supervised and unsupervised tasks. This algorithm treats each document as a semantic network. Structural dynamics of the network are used to extract keyphrases (key nodes) unsupervised. Experiments demonstrate the proposed algorithm averagely improves 50% in effectiveness and 30% in efficiency in unsupervised tasks and performs comparatively with supervised extractors. Moreover, by applying this algorithm to supervised tasks, we develop a classifier with an overall accuracy up to 80%.