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Named entity recognition of wheat diseases and pests is a key step to building a knowledge graph. Aiming at the problems of lack of training data, complex entity structure, diverse entity types and uneven entity distribution in wheat diseases and pests field, under the promise of fully mining the implicit knowledge, two data augmentation methods are used to expand sentence semantic information, and to construct the corpus WpdCNER (wheat pests and diseases Chinese named entity recognition) and the field lexicon WpdDict (wheat pests and diseases dictionary). And 16 categories of entities are defined with the field experts’ guidance. Meanwhile, Chinese named entity recognition model based on rules amendment WPD-RA (wheat pests and disease-rules amendment model) is proposed. This model is carried out entity recognition based on ALBERT+BiLSTM+CRF (a lite bi-directional encoder representation from transformer + bi-directional long short-term memory + conditional random field), and specific rules are defined to amend entity boundaries after recognition. The WPD-RA model achieves the best results with 94.72% precision, 95.23% recall, and 94.97% F1. Its precision is increased by 1.71 percentage points, recall is increased by 0.34 percentage points, and F1 is increased by 1.03 percentage points, compared with the model without rules. Experimental results show that the model can effectively recognize named entities in wheat diseases and pests field, and its performance is better than other models. The proposed model provides a reference idea for named entity recognition task in other fields such as food safety and biology.

In the food domain, text mining techniques are extensively employed to derive valuable insights from large volumes of text data, facilitating applications such as aiding food recalls, offering personalized recipes, and reinforcing food safety regulation. To provide researchers and practitioners with a comprehensive understanding of the latest technology and application scenarios of text mining in the food domain, the pertinent literature is reviewed and analyzed. Initially, the fundamental concepts, principles, and primary tasks of text mining, encompassing text categorization, sentiment analysis, and entity recognition, are elucidated. Subsequently, an analysis of diverse types of data sources within the food domain and the characteristics of text data mining is conducted, spanning social media, reviews, recipe websites, and food safety reports. Furthermore, the applications of text mining in the food domain are scrutinized from the perspective of various scenarios, including leveraging consumer food reviews and feedback to enhance product quality, providing personalized recipe recommendations based on user preferences and dietary requirements, and employing text mining for food safety and fraud monitoring. Lastly, the opportunities and challenges associated with the adoption of text mining techniques in the food domain are summarized and evaluated. In conclusion, text mining holds considerable potential for application in the food domain, thereby propelling the advancement of the food industry and upholding food safety standards. [ABSTRACT FROM AUTHOR]

The verified text data of wheat varieties is an important component of wheat germplasm information. To automatically obtain a structured description of the phenotypic and genetic characteristics of wheat varieties, the aim at solve the issues of fuzzy entity boundaries and overlapping relationships in unstructured wheat variety approval data, WGIE-DCWF (joint extraction model of wheat germplasm information entity relationship based on deep character and word fusion) was proposed. The encoding layer of the model deeply fused word semantic information and character information using the Transformer encoder of BERT. This allowed for the cascading fusion of contextual semantic feature information to achieve rich character vector representation and improve the recognition ability of entity features. The triple extraction layer of the model established a cascading pointer network, extracted the head entity, extracted the tail entity according to the relationship category, and decoded the output triplet. This approach improved the model's capability to extract overlapping relationships. The experimental results demonstrated that the WGIE-DCWF model performed exceptionally well on both the WGD (wheat germplasm dataset) and the public dataset DuIE. The WGIE-DCWF model not only achieved high performance on the evaluation datasets but also demonstrated good generalization. This provided valuable technical support for the construction of a wheat germplasm information knowledge base and is of great significance for wheat breeding, genetic research, cultivation management, and agricultural production., (© 2024. The Author(s).)