Abstract
AbstractLabeling documents with keyphrases is a tedious and expensive task. Most approaches to automatic keyphrases extraction rely on supervised learning and require manually labeled training data. In this paper we propose a fully unsupervised keyphrase extraction method, differing from the usual generic keyphrase extractor in the manner the keyphrases are formed. Our method begins by building topically related word clusters from which document keywords are selected, and then expands the selected keywords into syntactically valid keyphrases. We evaluate our approach on a Croatian document collection annotated by eight human experts, taking into account the high subjectivity of the keyphrase extraction task. The performance of the proposed method reaches up to F1 = 44.5%, which is outperformed by human annotators, but comparable to a supervised approach.KeywordsInformation extractionkeyphrase extractionunsupervised learningk-meansCroatian language
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