A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach
2014
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Given the large amounts of online textual documents available these days, e.g., news articles, weblogs, and scientific papers, effective methods for extracting keyphrases, which provide a high-level topic description of a document, are greatly needed. In this paper, we propose a supervised model for keyphrase extraction from research papers, which are embedded in citation networks. To this end, we design novel features based on citation network information and use them in conjunction with
doi:10.3115/v1/d14-1150
dblp:conf/emnlp/CarageaBGG14
fatcat:hinzsef3lzfd3irmhgkdm6t34e