UNPMC: Naïve approach to extract keyphrases from scientific articles

We describe our method for extracting keyphrases from scientific articles which we participate in the shared task of SemEval-2 Evaluation Exercise. Even though general-purpose term extractors along with linguistically-motivated analysis allow us to extract elaborated morphosyntactic variation forms of terms, a naïve statistic approach proposed in this paper is very simple and quite efficient for extracting keyphrases especially from well-structured scientific articles. Based on the characteristics of keyphrases with section information, we obtain 18.34% for f-measure using top 15 candidates. We also show further improvement without any complications and we discuss this at the end of the paper.