WikiRank: Improving Keyphrase Extraction Based on Background Knowledge
View Researcher's Other CodesDisclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).
Please contact us in case of a broken link from here
Authors | Vincent Ng, Yang Yu |
Journal/Conference Name | LREC 2018 - 11th International Conference on Language Resources and Evaluation |
Paper Category | Artificial Intelligence |
Paper Abstract | Keyphrase is an efficient representation of the main idea of documents. While background knowledge can provide valuable information about documents, they are rarely incorporated in keyphrase extraction methods. In this paper, we propose WikiRank, an unsupervised method for keyphrase extraction based on the background knowledge from Wikipedia. Firstly, we construct a semantic graph for the document. Then we transform the keyphrase extraction problem into an optimization problem on the graph. Finally, we get the optimal keyphrase set to be the output. Our method obtains improvements over other state-of-art models by more than 2% in F1-score. |
Date of publication | 2018 |
Code Programming Language | Python |
Comment |