The Comprehensive Analysis of the Effect of Chinese Word Segmentation on Fuzzy-Based Classification Algorithms for Agricultural Questions.

Fuzzy logic is the core method for handling uncertainty and vagueness of information in agricultural natural language processing, and it also plays a crucial role in word segmentation and text classification algorithms using the neural network. Word segmentation is often the primary step in Chinese text classification tasks and has a profound effect on the generation ability of classification algorithm-based fuzzy logic. However, the high complexity of text classification models structure and specificity of agricultural data take a great challenge to studying the effect of word segmentation. Although there have been several attempts to resolve this issue, the main effort focuses on word segment Precision or the generalization performance of multiple word segment methods for the same classification algorithm and does not involve agricultural text. To solve this problem from the perspective of rational analysis and empirical analysis, a comprehensive analysis has been made to study the effect of Chinese word segmentation on fuzzy-based classification algorithms for agricultural questions. It initially discusses the characteristics of agricultural questions for the subsequent analysis of the field adaptability of word segmentation and classification algorithms, employs fuzzy logic to convert the Chinese word segmentation task into a sequence labeling problem, and then analyzes the characteristics, techniques, and performance disparities of the seven mainstream open-source Chinese word segmentation integration tools at the current stage. Subsequently, an exploration has been conducted into the impact of Chinese word segmentation on the generalization performance of classification algorithms under the proposed unified model framework for text classification based on fuzzy logic. Finally, many experiments have been performed on the actual data crawled from typical agricultural websites to empirically study the differences and robustness of the effect of different word segmentation tools on classification performance, as well as the contribution of the external dictionary. Comparative experimental results show which word segmentation tools have a solid effect on classification performance and a strong robust effect on the typical text feature extraction layer for classification tasks, and the external dictionary have no significant effect on classification performance. The research results have essential reference significance for how to select appropriate word segmentation tools to deal with Chinese natural language processing tasks in future. [ABSTRACT FROM AUTHOR]