Abstract
Extracting definition sentences and hypernyms is the key step in knowledge graph construction as well as many other NLP applications. In this paper, we propose a novel supervised two-phase machine learning framework to solve both tasks simultaneously. Firstly, a joint neural network is trained to predict both definition sentences and hypernyms. Then a refinement model is utilized to further improve the performance of hypernym extraction. Experiment result shows the effectiveness of our proposed framework on a well-known benchmark.
This research was partially funded by ARC DPs 170103710 and 180103411, and D2DCRC DC25002 and DC25003.
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Sun, Y., Liu, S., Wang, Y., Wang, W. (2019). Extracting Definitions and Hypernyms with a Two-Phase Framework. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_57
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DOI: https://doi.org/10.1007/978-3-030-18590-9_57
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