Affiliations: School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, China
Correspondence:
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Corresponding author.Gaojian Xu, School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China. E-mail: xugj@ahau.edu.cn.
Note: [1] The work is supported by the Key Natural Science Research Projects in Universities in Anhui Province (Program No. 2022AH050896), Anhui University Students’ Innovation and Entrepreneurship Program Funded Projects (Program No. S202310364126) and A Cloud-modelled Method for Determining The Class Of Wheat Blast Disease Based On a Cloud Model (Program No. KJ2021721).
Abstract: There is scope to enhance agricultural measurement and control systems user interactivity, which typically necessitates training for users to perform specific operations successfully. With the continuous development of natural language semantic processing technology, it has become essential to augment the user-friendliness of multifaceted control and query operations in the agricultural measurement and control sector, ultimately leading to reduced operation costs for users. The study aims to focus on command parsing. The proposed AMR-OPO semantic parsing framework is based on the natural language understanding method of Abstract Meaning Representation of Rooted Markup Graphs (AMR). It transforms the user’s natural language inputs into structured ternary (OPO) statements (operation-place-object) and converts the corresponding parameters of the user’s input commands. The framework subsequently sends the transformed commands to the relevant devices via the IoT gateway. To tackle the intricate task of parsing instructions, we developed a BERT-BiLSTM-ATT-CRF-OPO entity recognition model. This model can detect and extract entities from agricultural instructions, and precisely populate them into OPO statements. Our model shows exceptional accuracy in instruction parsing, with precision, recall, and F-value all measuring at 92.13%, 93.12%, and 92.76%, correspondingly. The findings from our experiment reveal outstanding and precise performance of our approach. It is anticipated that our algorithm will enhance the user experience offered by agricultural measurement and control systems, while also making them more user-friendly.
Keywords: Natural language processing, abstract meaning representation, entity recognition, natural language understanding, human-computer interaction
