DFKI KeyWE: Ranking keyphrases extracted from scientific articles
Kathrin Eichler, Günter Neumann · 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