# Research on Named Entity Recognition Methods in Chinese Forest Disease Texts
## 1 Introduction  
Named entity recognition is a core task in the field of natural language processing. Its goal is to extract specific types of entities from text, such as people's names, place names, and organization names [1]. It plays a key role in the research of knowledge map construction, automatic question answering, and network searches. There are three basic kinds of named entity recognition methods: rule-based methods, statistical machine learning methods, and deep learning methods. With the development of deep learning technologies, such as long short-term memory (LSTM) and transformer, the performance of named entity recognition methods for the general domain has been greatly improved [2]. In particular, the transformer model processes the characters in the sequence in parallel, and uses the self-attention mechanism to directly model the relationship between the characters in the sentence instead of using the original cycle and convolution structure. It has better computational performance than RNNs and CNNs in tasks such as machine translation and named entity recognition. However, as the data in different fields have unique characteristics, and as there may be a lack of large-scale annotation data, research on named entity recognition in the general domain cannot be well-migrated to the proprietary domain. Therefore, scholars have carried out much exploratory research on named entity recognition in proprietary fields such as electronic medical records [3], bridge engineering [4], and military software detection [5]. Forest reserves are one of the most important resources for economic and social development, bringing rich economic, ecological, and social benefits. Forest diseases maydestroy large forests and cause poor tree growth, decline in yield and quality, and even the death of trees, resulting in economic losses and threatening the healthy development of the ecological environment. In history, serious diseases have been treated continuously. Their impact increases with the increase in plantation area. For example, Valsa sordida and poplar canker in northern China and early defoliation disease of Larch and Lecanosticta acicola in southern China pose serious threats to forestry production and ecology. Due to the concealment of forest diseases, it is difficult to distinguish and control them. Research on forest diseases and how to control these have always been a focus of attention. With the development of forestry information, information technology has become more widely used in the field of forest diseases, and substantial text data related to forest diseases have been accumulated, mostly stored in an unstructured form. Research on named entity recognition technology for forest disease-related texts can lay the foundation for the scientific construction of high-quality forest disease intelligent question-and-answer systems, recommendation systems, intelligent searches, and other downstream applications. The text in the field of forest diseases lacks large-scale annotation data sets. Entities have nested relationships. In addition, there are numerous domain-specific entity concepts and rare words in the text, such as diseases, damage parts, and so on. Named entity recognition of forest disease texts still needs to be explored in depth. For forestry texts, researchers have successively proposed a rule-based method [6], BCC-P method [7], etc., but these methods do not fully consider the characteristics of domain texts. Moreover, there is less research on named entity recognition of Chinese forest disease texts. Previous research has achieved better performance of named entity recognition in proprietary fields by multi-feature embedding. For example, in the field of Chinese electronic medical records, researchers embedded character feature and glyph feature as input, and had good results in the self-built data set [3]. In this study, we examine the named entity recognition of forest disease texts.