WordTopic-MultiRank: A New Method for Automatic Keyphrase Extraction
Fan Zhang, Bo Peng · 2015
Automatic keyphrase extraction aims to pick out a set of terms as a representa-tion of a document without manual assign-ment efforts. Supervised and unsupervised graph-based ranking methods have been s-tudied for this task. However, previous methods usually computed importance s-cores of words under the assumption of single relation between words. In this work, we propose WordTopic-MultiRank as a new method for keyphrase extraction, based on the idea that words relate with each other via multiple relations. First we treat various latent topics in documents as heterogeneous relations between words and construct a multi-relational word net-work. Then, a novel ranking algorithm, named Biased-MultiRank, is applied to s-core the importance of words and topics si-multaneously, as words and topics are con-sidered to have mutual influence on each other. Experimental results on two differ-ent data sets show the outstanding perfor-mance and robustness of our proposed ap-proach in automatic keyphrase extraction task. 1