DFKI-LT - DFKI KeyWE: Ranking keyphrases extracted from scientific articles

Kathrin Eichler, GŁnter Neumann
DFKI KeyWE: Ranking keyphrases extracted from scientific articles
2 Proceedings of the Fifth International Workshop on Semantic Evaluations, Uppsala, Sweden, ACL, 2010
 
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.
 
Files: BibTeX, dilia_acl2010_FINAL.pdf