Abstract
Extracting keyphrases from documents helps to reduce the document information and further assist in information retrieval. In this paper, we construct a multi-relational graph by considering heterogeneous latent word relations (the co-occurrence and the semantic) in a document. Then we optimize the random walks on the multi-relational graph to determine the importance of each node to further generate keyphrases. Experimental results show that our method outperforms the previous methods.
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Acknowledgements
This work is supported in part by Jiangsu Provincial Natural Science Foundation of China under Grant BK20171447, Jiangsu Provincial University Natural Science Research of China under Grant 17KJB520024, and Nanjing University of Posts and Telecommunications under Grant No. NY215045.
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Chen, W., Liu, Z., Shi, W., Yu, J.X. (2018). Keyphrase Extraction Based on Optimized Random Walks on Multiple Word Relations. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10988. Springer, Cham. https://doi.org/10.1007/978-3-319-96893-3_27
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