Abstract
Using the named entity’s type tag to construct a unique vector for a class of named entities can solve the problem that named entities are too scattered in the semantic space. In the relation extraction task, the relation of the specified entity pair in each sentence needs to be extracted. However, the general deep learning model cannot reflect the usefulness of the entity pair and its type tag effectively. In order to solve this problem, this paper studies the characteristics of named entity’s type tag, and proposes a word vector optimization relation extraction model and a parallel structure optimization relation extraction model based on the type tags of named entities. Experiments on COAE 2016 task 3 show that the parallel structure optimization model based on the named entity’s type tag improves the relation extraction effect effectively.
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Acknowledgments
This work was supported by grants from National Nature Science Foundation of China (No. 61772081), National Nature Science Foundation of China (No. 61602044), and Scientific Research Project of Beijing Educational Committee (No. KM201711232022).
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Zhang, Y., Zhang, Y., Huang, G., Guo, Z. (2018). Optimizing Relation Extraction Based on the Type Tag of Named Entity. In: Hong, JF., Su, Q., Wu, JS. (eds) Chinese Lexical Semantics. CLSW 2018. Lecture Notes in Computer Science(), vol 11173. Springer, Cham. https://doi.org/10.1007/978-3-030-04015-4_45
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DOI: https://doi.org/10.1007/978-3-030-04015-4_45
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