In this paper we analyze the effectiveness of using linguistic knowledge from coreference and anaphora resolution for improving the performance for supervised keyphrase extraction. In order to verify the impact of these features, we define a baseline keyphrase extraction system and evaluate its performance on a standard dataset using different machine learning algorithms. Then, we consider new sets of features by adding combinations of the linguistic features we propose and we evaluate the new performance of the system. We also use anaphora and coreference resolution to transform the documents, trying to simulate the cohesion process performed by the human mind. We found that our approach has a slightly positive impact on the performance of automatic keyphrase extraction, in particular when considering the ranking of the results.