Paper: DFKI KeyWE: Ranking Keyphrases Extracted from Scientific Articles

ACL ID S10-1031
Title DFKI KeyWE: Ranking Keyphrases Extracted from Scientific Articles
Venue Workshop on Semantic Evaluations (SemEval)
Session
Year 2010
Authors

A central issue for making the content of a scientific document quickly acces- sible to a potential reader is the extrac- tion 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 ex- traction and a supervised ranking algo- rithm 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.