Shi, Hanyu and Qu, Weiguang and Wei, Tingxin and Zhou, Junsheng and Long, Yunfei and Gu, Yanhui and Li, Bin (2021) Hybrid Neural Network for Automatic Recovery of Elliptical Chinese Quantity Noun Phrases. Computers, Materials and Continua, 69 (3). pp. 4113-4127. DOI https://doi.org/10.32604/cmc.2021.019518
Shi, Hanyu and Qu, Weiguang and Wei, Tingxin and Zhou, Junsheng and Long, Yunfei and Gu, Yanhui and Li, Bin (2021) Hybrid Neural Network for Automatic Recovery of Elliptical Chinese Quantity Noun Phrases. Computers, Materials and Continua, 69 (3). pp. 4113-4127. DOI https://doi.org/10.32604/cmc.2021.019518
Shi, Hanyu and Qu, Weiguang and Wei, Tingxin and Zhou, Junsheng and Long, Yunfei and Gu, Yanhui and Li, Bin (2021) Hybrid Neural Network for Automatic Recovery of Elliptical Chinese Quantity Noun Phrases. Computers, Materials and Continua, 69 (3). pp. 4113-4127. DOI https://doi.org/10.32604/cmc.2021.019518
Abstract
In Mandarin Chinese, when the noun head appears in the context, a quantity noun phrase can be reduced to a quantity phrase with the noun head omitted. This phrase structure is called elliptical quantity noun phrase. The automatic recovery of elliptical quantity noun phrase is crucial in syntactic parsing, semantic representation and other downstream tasks. In this paper, we propose a hybrid neural network model to identify the semantic category for elliptical quantity noun phrases and realize the recovery of omitted semantics by supplementing concept categories. Firstly, we use BERT to generate character-level vectors. Secondly, Bi-LSTM is applied to capture the context information of each character and compress the input into the context memory history. Then CNN is utilized to capture the local semantics of n-grams with various granularities. Based on the Chinese Abstract Meaning Representation (CAMR) corpus and Xinhua News Agency corpus, we construct a hand-labeled elliptical quantity noun phrase dataset and carry out the semantic recovery of elliptical quantity noun phrase on this dataset. The experimental results show that our hybrid neural network model can effectively improve the performance of the semantic complement for the elliptical quantity noun phrases.
Item Type: | Article |
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Uncontrolled Keywords: | Elliptical quantity noun phrase; semantic complement; neural network |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 04 Aug 2023 15:18 |
Last Modified: | 30 Oct 2024 16:25 |
URI: | http://repository.essex.ac.uk/id/eprint/34652 |
Available files
Filename: TSP_CMC_19518.pdf
Licence: Creative Commons: Attribution 4.0