Unsupervised keyphrase extraction based ranking algorithm for opinion articles

Abstract

Keyphrase extraction is to select the most representative phrases within a given text. While supervised methods require a large amount of training data, unsupervised methods can perform without prior knowledge such as language. In this paper, we propose a ranking algorithm based on unsupervised keyphrase extraction and develop a framework for retrieving opinion articles. Since the proposed algorithm uses an unsupervised method, it can be employed to multi-language systems. Moreover, our proposed ranking algorithm measures the importance in three aspects, the amount of information within articles, representativeness of sentences, and frequency of words. Our framework shows better performance than previous algorithms in terms of precision and NDCG. © 2013 Springer Science+Business Media Dordrecht(Outside the USA).