Learning Algorithms for Keyphrase Extraction
Information Retrieval
Improved automatic keyword extraction given more linguistic knowledge
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Listwise approach to learning to rank: theory and algorithm
Proceedings of the 25th international conference on Machine learning
Reducing long queries using query quality predictors
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A ranking approach to keyphrase extraction
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Single document keyphrase extraction using neighborhood knowledge
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Domain-specific keyphrase extraction
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Coherent keyphrase extraction via web mining
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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
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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Keyphrases extracted from news articles can be used to concisely represent the main content of news events. In this paper, we first present several criteria of high-quality news keyphrases. After that, in order to integrate those criteria into the keyphrase extraction task, we propose a novel formulation which coverts the task to a learning to rank problem. Our approach involves two phases: selecting candidate keyphrases and ranking all possible sub-permutations among the candidates. Three kinds of feature sets: lexical feature set, locality feature set and coherence feature set are introduced to rank the candidates, and then the best sub-permutation provides the keyphrases. The proposed method is evaluated on a multi-news collection and experimental results verify that our proposed method is effective to extract coherent news keyphrases.