Keyphrases represent the main topics a text is about. In this article, we introduce SemGraph, an unsupervised
algorithm for extracting keyphrases from a collection of texts based on a semantic relationship graph.
The main novelty of this algorithm is its ability to identify semantic relationships between words whose
presence is statistically significant. Our method constructs a co-occurrence graph in which words appearing
in the same document are linked, provided their presence in the collection is statistically significant
with respect to a null model. Furthermore, the graph obtained is enriched with information from WordNet.
We have used the most recent and standardized benchmark to evaluate the system ability to detect the keyphrases
that are part of the text. The result is a method that achieves an improvement of 5.3% and 7.28% in F measure
over the two labeled sets of keyphrases used in the evaluation of SemEval-2010.