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.
