|Table of Contents|

Research on Wheat Question Classification Model Based on Dual-Granularity(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2025年01期
Page:
100-108
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Research on Wheat Question Classification Model Based on Dual-Granularity
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)
Keywords:
wheat community Q&; A; multi-scale CNN; attention mechanism; question classification
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2025.01.013
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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Last Update: 2025-02-15