Semi-Supervised Keyphrase Extraction on Scientific Article using Fact-based Sentiment

Felix Christian Jonathan, Oscar Karnalim


Most scientific publishers encourage authors to provide keyphrases on their published article. Hence, the need to automatize keyphrase extraction is increased. However, it is not a trivial task considering keyphrase characteristics may overlap with the non-keyphrase’s. To date, the accuracy of automatic keyphrase extraction approaches is still considerably low. In response to such gap, this paper proposes two contributions. First, a feature called fact-based sentiment is proposed. It is expected to strengthen keyphrase characteristics since, according to manual observation, most keyphrases are mentioned in neutral-to-positive sentiment. Second, a combination of supervised and unsupervised approach is proposed to take the benefits of both approaches. It will enable automatic hidden pattern detection while keeping candidate importance comparable to each other. According to evaluation, fact-based sentiment is quite effective for representing keyphraseness and semi-supervised approach is considerably effective to extract keyphrases from scientific articles.


fact-based sentiment; semi-supervised approach; keyphrase extraction; scientific article; deep belief network

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