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Review of Text Neural Semantic Parsing Methods |
Shen Lingyun1,2,Le Xiaoqiu1,2() |
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China 2Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China |
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Abstract [Objective] This paper summarizes and comments on the research methods of text semantic parsing with neural networks in the past decade. [Coverage] With Google Scholar and CNKI as the data retrieval platforms, and “Neural Semantic Parsing” as the keywords, all relevant papers and their important citations from 2010 to 2022 were retrieved for analysis. [Methods] The paper classified the existing neural semantic parsing methods according to the technical path, explained the basic ideas of each technical path, compared and analyzed the similarities and differences of each technology method in data, performance, application goals, etc., and summarized the existing problems and development tendency of text neural semantic parsing technology. [Results] Neural semantic parsing methods could be summarized into three types, sequence to sequence method, intermediate form based method, and semantic unit decomposition and combination method. The latter two methods are improvements to the first method. At present, intermediate representations such as semantic sketch, canonical utterance and few-shot neural semantic parsing are the main research focuses. [Limitations] The paper mainly summarized and analyzed the existing research ideas from the methodology, but does not elaborate the internal implementation mechanism of the neural semantic parsing models. [Conclusions] The neural semantic parsing method gains the best performance in text semantic parsing at present. The current popular practice is to design targeted neural network models for specific applications. But the effect of semantic parsing is still far from the practical application.
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Received: 13 October 2022
Published: 02 February 2024
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Corresponding Authors:
Le Xiaoqiu,ORCID:0000-0002-7114-5544,E-mail:lexq@mail.las.ac.cn。
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