  Keyphrase extraction is the task of finding several interesting phrases in a
text document, which provide a list of the main topics within the document.
Most existing graph-based models use co-occurrence links as cohesion indicators
to model the relationship of syntactic elements. However, a word may have
different forms of expression within the document, and may have several
synonyms as well. Simply using co-occurrence information cannot capture this
information. In this paper, we enhance the graph-based ranking model by
leveraging word embeddings as background knowledge to add semantic information
to the inter-word graph. Our approach is evaluated on established benchmark
datasets and empirical results show that the word embedding neighborhood
information improves the model performance.
