We investigate how the automatic identification of noun compounds and named entities can contribute to keyphrase extraction and we also show how previously identified noun compounds affect named entity recognition and vice versa, how noun compound detection is supported by identified named entities. Our experiments demonstrate that already known noun compounds yield better performance in named entity recognition and already known named entities enhance noun compound detection. The integration of noun compound and named entity related features into a keyphrase extractor also proveis to be more effective than the model not including them. Our results indicate that the above features tend to be beneficial in several NLP-related tasks.
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