Abstract
Semantic hierarchies construction means to build structure of concepts linked by hypernym-hyponym (“is-a”) relations. A major challenge for this task is the automatic discovery of hypernym-hyponym (“is-a”) relations. We propose a fusion learning architecture based on word embeddings for constructing semantic hierarchies, composed of discriminative generative fusion architecture and a very simple lexical structure rule for assisting, getting an F1-score of 74.20% with 91.60% precision-value, outperforming the state-of-the-art methods on a manually labeled test dataset. Subsequently, combining our method with manually-built hierarchies can further improve F1-score to 82.01%. Besides, the fusion learning architecture is language-independent.
T. Jiang—Ph.D Student.
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Notes
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Baidubaike (https://baike.baidu.com/) is one of the largest Chinese encyclopedias.
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Funding
The research in this paper is supported by National Natural Science Foundation of China (No. 61632011, No. 61772156), National High-tech R&D Program (863 Program) (No. 2015AA015407).
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Jiang, T., Liu, M., Qin, B., Liu, T. (2017). Constructing Semantic Hierarchies via Fusion Learning Architecture. In: Wen, J., Nie, J., Ruan, T., Liu, Y., Qian, T. (eds) Information Retrieval. CCIR 2017. Lecture Notes in Computer Science(), vol 10390. Springer, Cham. https://doi.org/10.1007/978-3-319-68699-8_11
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