{"type":"ScholarlyArticle","title":"Integrating Semantic Relatedness and Words' Intrinsic Features for Keyword Extraction","authors":[{"name":"Wei Zhang","slug":"wei-zhang-5","given":"Wei","family":"Zhang","affiliation":"Department of Computer Sci. and Tech. , Tsinghua University , Beijing , China"},{"name":"Wei Feng","slug":"wei-feng","given":"Wei","family":"Feng","affiliation":"Department of Computer Sci. and Tech. , Tsinghua University , Beijing , China"},{"name":"Jianyong Wang","slug":"jianyong-wang","given":"Jianyong","family":"Wang","affiliation":"Department of Computer Sci. and Tech. , Tsinghua University , Beijing , China"}],"abstract":"Keyword extraction attracts much attention for its significant role in various natural language processing tasks. While some existing methods for keyword extraction have considered using single type of semantic relatedness between words or inherent attributes of words, almost all of them ignore two important issues: 1) how to fuse multiple types of semantic relations between words into a uniform semantic measurement and automatically learn the weights of the edges between the words in the word graph of each document, and 2) how to integrate the relations between words and words' intrinsic features into a unified model. In this work, we tackle the two issues based on the supervised random walk model. We propose a supervised ranking based method for keyword extraction, which is called SEAFARER. It can not only automatically learn the weights of the edges in the unified graph of each document which includes multiple semantic relations but also combine the merits of semantic relations of edges and intrinsic attributes of nodes together. We conducted extensive experimental study on an established benchmark and the experimental results demonstrate that SEAFARER outperforms the state-of-the-art supervised and unsupervised methods.","doi":null,"url":"https://ijcai.org/proceedings/2013/328","pdf":"https://ijcai.org/proceedings/2013/0328.pdf","markdown":"https://ijcai.org/proceedings/2013/328.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/328","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":328,"pages":{"first":2225,"last":2230},"keywords":[],"videos":[],"references":[{"position":1,"raw":"[Backstrom and Leskovec, 2011] Lars Backstrom and Jure Leskovec. 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