Keyphrases are words that capture the main topics of a document. Extracting high- quality keyphrases can benefit various natural language processing (NLP) applications: in text summarization, keyphrases are useful as a form of semantic metadata indicating the significance of sentences and paragraphs, in which they appear; in both text categorization and document clustering, keyphrases offer a means of term dimensionality reduction, and have been shown to improve system efficiency and accuracy; and for search engines, keyphrases can supplement full-text indexing and assist users in formulating queries.
WING-NUS at SemEval-2017 Task 10: Keyphrase Identification and Classification as Joint Sequence Labeling Conference
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval@ACL), ACL, Vancouver, Canada, 2017.
Automatic keyphrase extraction from scientific articles Conference
Language Resources and Evaluation, 2012.
Evaluating N-gram based Evaluation Metrics for Automatic Keyphrase Extraction Conference
Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010), 2010.
SemEval-2010 Task 5: Automatic Keyphrase Extraction from Scientific Articles Conference
Proceedings of SemEval-2, 2010.
WINGNUS: Keyphrase Extraction Utilizing Document Logical Structure Conference
Proceedings of the ACL 2010 Workshop on Evaluation Exercises on Semantic Evaluation (SemEval 2010), 2010.
Re-examining Automatic Keyphrase Extraction Approaches in Scientific Articles Conference
Proceedings of the ACL-IJCNLP 2009 Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications, 2009.
Keyphrase Extraction in Scientific Publications Conference
Proceedings of International Conference on Asian Digital Libraries (ICADL '07), 2007.