# Research on Named Entity Recognition Methods in Chinese Forest Disease Texts
## 2 Related Work  
Named entity recognition (NER) was formally established as a sub-task of information extraction in the Sixth Message Understanding Conference (MUC-6) [8], which stipulates that named entities include personal names, place names, and organization names. In the subsequent MET-2 [9] of MUC-7 and a series of international conferences, including IEER-99, CoNLL-2002, CoNLL-2003, IREX, and LREC, named entity recognition was regarded as a designated task in the field of information extraction. Moreover, the goals related to named entities are expanding. There are three basic kinds of named entity recognition methods: rule-based methods, statistical machine learning methods, and deep learning methods. The methods based on rules rely on the manual construction of dictionaries and knowledge bases, and mostly adopt rules manually constructed by language experts. The selected features include direction words, punctuation, statistical information, and other methods to match patterns with strings. The portability of these methods is poor, as they often depend on specific fields and text features. Statistical machine learning methods typically use a manually labeled corpus for training. For new fields in this method, only a small amount of modification is needed for training. Typical machine learning models include maximum entropy (ME) [10], support vector machine (SVM) [11], hidden Markov model (HMM) [12], and conditional random fields (CRF) [13]. In recent years, named entity recognition methods based on deep learning have become the mainstream. Deep learning models are end-to-end models [14]. Deep neural networks can carry out non-linear transformations on data, then automatically learn more complex features to complete the training and prediction tasks of multi-layer neural networks. [PERSON] et al. [15] first proposed a named entity recognition method based on neural networks. This method limits the use of context to a fixed window size around each word, abandons the useful long-distance relationships between words, and cannot resolve the problem of long-distance dependence. With the progress of recurrent neural networks (RNNs), in terms of structure and rapid development of hardware performance, the training efficiency of deep learning has made great breakthroughs. The use of recurrent neural networks has become increasingly common. Variants of cyclic neural networks, long short-term memory (LSTM), and gated recurrent units (GRUs) have made breakthroughs in the field of natural language processing (NLP). LSTM has a strong ability to extract long-term sequence features. [PERSON] et al. [16] first applied the bidirectional LSTM-CRF model to the benchmark sequence-marked data set of natural language processing. Bidirectional LSTM can preserve long-term memory and make use of past and future sequence information. Moreover, this model adds CRF as a decoding tool. Their experimental results showed that this model has less dependence on the word embedding and achieved a good training effect. [PERSON] et al. [17] proposed a deep-seated recurrent neural network for sequence labeling, which uses GRUs to encode morphological and contextual information at the character and word levels, and applies a CRF field layer to predict labels. GRUs have higher calculation speeds, as they simplify the gating unit on the basis of similar accuracy to that of LSTM. Their model obtained a 91.20% F1 value on the CoNLL2003 English data set, and effectively solved the problem of cross-language joint training. The transformer model was proposed by [PERSON] et al. [18], which constructs the coding layer and decoding layer through the use of a multi-head attention mechanism. Through parameter matrix mapping, the attention operation is carried out. Then, the process is repeated many times. Finally, the results are spliced to obtain the global features. As the transformer model has the advantages of parallel computing and deep architecture, it has been widely used in named entity recognition tasks.