Research Area: Machine Learning
Extracting keyphrases from documents automatically is an important and interesting task since keyphrases provide a quick summarization for documents. Although lots of efforts have been made on keyphrase extraction, most of the existing methods (the co-occurrence-based methods and the statistic-based methods) do not take semantics into full consideration. The co-occurrence-based methods heavily depend on the co-occurrence relations between two words in the input document, which may ignore many semantic relations. The statistic-based methods exploit the external text corpus to enrich the document, which introduce more unrelated relations inevitably. In this paper, we propose a novel approach to extract keyphrases using knowledge graphs, based on which we could detect the latent relations of two keyterms (i.e., noun words and named entities) without introducing many noises. Extensive experiments over real data show that our method outperforms the state-of-the-art methods including the graph-based co-occurrence methods and statistic-based clustering methods.
Keywords:
Keyphrase Extraction
Knowledge Graphs
Machine Learning
Deep Learning
unsupervised
Graph-Based Keyphrase Extraction
clustering methods
Author(s) Name: Wei Shi, Weiguo Zheng, Jeffrey Xu Yu, Hong Cheng & Lei Zou
Journal name: Data Science and Engineering
Conferrence name:
Publisher name: Springer
DOI: 10.1007/s41019-017-0055-z
Volume Information: volume 2, pages: 275–288 (2017)
