Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A ranking approach to keyphrase extraction
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Unsupervised approaches for automatic keyword extraction using meeting transcripts
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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
Re-examining automatic keyphrase extraction approaches in scientific articles
MWE '09 Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications
Human-competitive tagging using automatic keyphrase extraction
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Automatic keyphrase extraction from scientific articles
Language Resources and Evaluation
| Hi-index | 0.00 |
A central issue for making the content of a scientific document quickly accessible to a potential reader is the extraction of keyphrases, which capture the main topic of the document. Keyphrases can be extracted automatically by generating a list of keyphrase candidates, ranking these candidates, and selecting the top-ranked candidates as keyphrases. We present the KeyWE system, which uses an adapted nominal group chunker for candidate extraction and a supervised ranking algorithm based on support vector machines for ranking the extracted candidates. The system was evaluated on data provided for the SemEval 2010 Shared Task on Keyphrase Extraction.