[1]赵新玥,陈美凤,张 静,等.基于双粒度的小麦问句分类模型研究[J].南京师大学报(自然科学版),2025,48(01):100-108.[doi:10.3969/j.issn.1001-4616.2025.01.013]
　Zhao Xinyue,Chen Meifeng,Zhang Jing,et al.Research on Wheat Question Classification Model Based on Dual-Granularity[J].Journal of Nanjing Normal University(Natural Science Edition),2025,48(01):100-108.[doi:10.3969/j.issn.1001-4616.2025.01.013]
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基于双粒度的小麦问句分类模型研究()
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《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
48
期数:
2025年01期
页码:
100-108
栏目:
计算机科学与技术
出版日期:
2025-02-15

文章信息/Info

Title:
Research on Wheat Question Classification Model Based on Dual-Granularity
文章编号:
1001-4616(2025)01-0100-09
作者:
赵新玥1; 陈美凤1; 张 静1; 王静茹1; 宋云胜1; 2
(1.山东农业大学信息科学与工程学院,山东 泰安 271018)
(2.农业农村部黄淮海智慧农业技术重点实验室,山东 泰安 271018)
Author(s):
Zhao Xinyue1; Chen Meifeng1; Zhang Jing1; Wang Jingru1; Song Yunsheng1; 2
(1.School of Information Science and Engineering,Shandong Agricultural University,Taian 271018,China)
(2.Huang Huaihai Key Laboratory of Intelligent Agriculture Technology Ministry of Agriculture and Rural Affairs,Taian 271018,China)
关键词:
小麦社区问答; 多尺度卷积; 注意力机制; 问句分类
Keywords:
wheat community Q&; A; multi-scale CNN; attention mechanism; question classification
分类号:
TP391
DOI:
10.3969/j.issn.1001-4616.2025.01.013
文献标志码:
A
摘要:
针对现阶段小麦问答社区的问句文本存在噪声、特征稀疏以及专业性强等问题,从词和字双粒度特征出发,提出了一种基于双粒度的小麦问句分类模型. 为有效缓解农业问句语义特征稀疏的问题,采用基于字粒度和词粒度的双分支架构,并引入交互注意力机制获取词粒度和字粒度交互特征信息以实现不同粒度信息表达文本语义的一致性,最后融合双粒度特征及其交互特征构建分类模型. 同时,在输入层添加农业字典和加载停用词表进行分词和分字,有效解决小麦社区问句文本专业性强和数据噪声问题. 与现有六种主流农业社区问句分类模型相比,该模型在整体分类性能上表现最优,且在各类别上综合性能优于其他模型. 本研究有助于提高小麦种植社区问答系统的性能,并积极推动智能农业推广进程、助力乡村振兴.
Abstract:
In response to the issues of noise,feature sparsity,and strong agricultural expertise in the question texts of the current wheat Q&A community,a wheat question classification model based on dual-granularity features is proposed. To effectively alleviate the problem of semantic feature sparsity in agricultural questions,a dual-branch architecture is employed,extracting both character-level and word-level features in separate branches. An interactive attention mechanism is introduced to capture the interaction between word-level and character-level features,ensuring the consistency of semantic representation across different granularities. Finally,the model integrates character-level,word-level,and their interactive features to construct a robust classification framework. Additionally,an agricultural dictionary and a stop words list are incorporated at the input layer to enhance word segmentation and character splitting,addressing the challenges of high domain specificity and data noise in wheat community question texts. Compared to six existing mainstream agricultural community question classification models,this model demonstrates superior overall classification performance and improved comprehensive performance across various categories. This research contributes to enhancing the performance of question-and-answer systems in wheat farming communities and actively promotes the advancement of smart agriculture,support rural revitalization efforts.

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备注/Memo

备注/Memo:
收稿日期:2024-06-07.
基金项目:山东省自然科学基金面上项目(ZR2020MF146).
通讯作者:宋云胜,博士,讲师,研究方向:大规模机器学习,智能数据分析与处理,农业大数据. E-mail:sys_sd@126.com
更新日期/Last Update: 2025-02-15