Machine Learning
KEA: practical automatic keyphrase extraction
Proceedings of the fourth ACM conference on Digital libraries
Learning Algorithms for Keyphrase Extraction
Information Retrieval
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
Finding advertising keywords on web pages
Proceedings of the 15th international conference on World Wide Web
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Keyword extraction for contextual advertisement
Proceedings of the 17th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
CollabRank: towards a collaborative approach to single-document keyphrase extraction
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Enhancing Keyword Search with a Keyphrase Index
Advances in Focused Retrieval
Graph-based keyword extraction for single-document summarization
MMIES '08 Proceedings of the Workshop on Multi-source Multilingual Information Extraction and Summarization
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Exploratory undersampling for class-imbalance learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Keyword extraction for social snippets
Proceedings of the 19th international conference on World wide web
Keyphrase extraction in scientific publications
ICADL'07 Proceedings of the 10th international conference on Asian digital libraries: looking back 10 years and forging new frontiers
Automatic keyphrase extraction via topic decomposition
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
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While supervised learning algorithms hold much promise for automatic keyphrase extraction, most of them presume that the samples are evenly distributed among different classes as well as drawn from an identical distribution, which, however, may not be the case in the real-world task of extracting keyphrases from documents. In this paper, we propose a novel supervised keyphrase extraction approach which deals with the problems of class-imbalanced and non-identical data distributions in automatic keyphrase extraction. Our approach is by nature a stacking approach where meta-models are trained on balanced partitions of a given training set and then combined through introducing meta-features describing particular keyphrase patterns embedded in each document. Experimental results verify the effectiveness of our approach.