# A Novel Named Entity Recognition Algorithm for Hot Strip Rolling Based on BERT-Imseq2 seq-CRF Model
## 1 Introduction  
The steel industry is one of the pillar industries supporting the rapid development of economy and has achieved rapid growth [1]. However, with the advent of the era of intelligence and data, steel manufacturing is facing many difficulties in on-site data management, storage, and application [2; 3; 4]. Hot strip rolling is an important scene in the field of steel manufacturing. There are a lot of work logs and technical documents in actual production. These field texts contain various professional information such as rolling process, equipment accuracy, and product quality. How to accurately and effectively extract domain information with application value from unstructured data has gradually become a hot spot in the industry [5; 6]. In 2012, Google proposed the concept of a knowledge graph, which has rapidly fermented in recent years, becoming a hot topic of discussion in many fields, and also pointed out a way for the transformation and the steel industry upgrading. The construction of a knowledge graph mainly includes knowledge extraction, knowledge update, knowledge processing, and knowledge application. In addition, knowledge extraction is also called information extraction, which aims to make targeted selection of unstructured data, determine the domain entities worthy of attention, and complete the type annotation and relationship binding. Then, Named Entity Recognition (NER) is mainly used to locate important text entities and predict the corresponding labelling entity type. The early NER algorithm is mainly based on rules or statistical machine learning [7]. The rule-based entity recognition method mainly relies on the artificially constructedrulebase, the recognition accuracy is high, but the execution efficiency is too low and the portability is poor. Later, statistical machine learning gradually replaced rule-based methods. For example, Conditional Random Field (CRF) improved the accuracy of model prediction by training massive data, and got rid of the problem of manual dependence to the greatest extent. However, the defect of the low generalization ability of the model is still not fundamentally solved. In recent years, with the development of computer technology, NER based on deep learning has gradually become the research hotspot. Compared with statistical machine learning methods, the recognition algorithm based on deep learning has high execution efficiency, high speed, and strong model generalization ability [8]. Taking advantage of parallel computing, Convolutional Neural Networks (CNN) can quickly complete the task of entity recognition. However, due to the limitation of the model structure, it cannot rely on long-distance text information to improve the labeling effect. The sequential structure of Recurrent Neural Networks (RNN) is similar to text representation, which can effectively capture textual context information, but there is the problem of gradient explosion or gradient disappearance. Then, Bi-directional Long and Short Term Memory Networks (BiLSTM) and Bi-directional Gated Recurrent Unit (BiGRU) are used to improve and optimize RNN. They effectively avoid gradient disappearance and gradient explosion through clever gating design, and mine text dependencies in both directions, becoming the preferred method for information extraction in the era of big data. In addition, NER is also integrating the information advantages of unsupervised learning, combining Bidirectional Encoder Representation from Transformers [9] (BERT) or A Lite BERT [10] (ALBERT) to enhance the semantic understanding of domain texts from a new perspective. The self-attention mechanism is used to mine the associated information in the unstructured data in both directions, which greatly improves the model recognition accuracy and application feasibility.