# A Novel Named Entity Recognition Algorithm for Hot Strip Rolling Based on BERT-Imseq2 seq-CRF Model
## 3 Model Design
### Analysis of Experimental Results  
In order to verify the considerable feasibility of the model, the paper firstly uses a third-party domain data set (Weibo dataset and news dataset) to conduct preliminary experiments on the model. The data set is divided into training set and test set according to the ratio of _7:3_, and several related models are selected as controls. The improved seq2 seq model is recorded as Imseq2 seq. The experimental results are shown in Table 3. The original seq2 seq model only sends the semantic vector C to the decoder, which covers relatively little text information, and it is difficult to accurately predict labels. The experimental effect is even far worse than the CRF model, and the F1-Score is only about 15%. The improved seq2 seq model makes full use of the output information and semantic vector of the encoder. Combined with the CRF model, the comprehensive experimental effect is more impressive than BiLSTM-CRF. After adding the BERT preprocessing model, the F1-Score increases to about 89.5%. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tag** & **Head** & **Middle** & **End** \\ \hline Position & B-POS & I-POS & E-POS \\ Index & B-IND & I-IND & E-IND \\ Parameter & B-PAR & I-PAR & E-PAR \\ Area & B-ARE & I-ARE & E-ARE \\ Object & B-OBJ & I-OBJ & E-OBJ \\ Defect Shape & B-SHA & I-SHA & E-SHA \\ Evaluation Aspect & B-EVA & I-EVA & E-EVA \\ Defect & B-DEF & I-DEF & E-DEF \\ Else & O & O & O \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Marking form of hot strip rolling. \begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration** & **Version/Value** \\ \hline Python & 3.6.1 \\ Pytorch & 1.5.1 \\ Batch size & 16 \\ Seq\_max\_len & 512 \\ Dropout rate & 0.5 \\ Learning Rate & 0.001 \\ LSTM\_dim & 256 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental environment and parameter configuration. Because the entity names contained in the third-party dataset are only person name (PER), local name (LOC) and organization (ORG), the entity types are relatively rare and the sentence structure is simple. The recognition difficulty is not high, and it is difficult to reflect the advantages of the model. Next, the model is applied to the task of entity recognition in hot strip rolling. The text expression of hot strip rolling is specialized, including entity types and entity names in many fields. At the same time, there are some entities with long names or nested entities, such as'spindle main motor torque','steel shifting impact fluctuation', 'outlet anti arching roll set pressure', and'measured flow deviation of rack cooling water', which undoubtedly increases the difficulty of entity identification. Similarly, in order to verify the feasibility and advantages of the model, relevant models are selected as the control experiment. The model identification results for the hot strip rolling data set are shown in Table 4. Combining WeiBo dataset, news dataset, and the hot strip rolling process accuracy dataset, it is not difficult to draw the following conclusions: CRF is a classic machine learning model, which realizes the information labelling by adding constraints. The angle of processing information is relatively single, and the F1-Score is only 77.30%; the seq2 seq model only transmits the paragraph compression information obtained by the encoder to the decoding layer, and the amount of information is greatly reduced, which is far from satisfying the labeling task of a single Chinese character, and the recognition effect is extremely poor; in addition, the encoder part of the Imseq2 seq model adopts BiLSTM. The decoding layer adopts unidirectional LSTM and combines the Teacher-Forcing mechanism to randomly correct the prediction results, and performs secondary prediction based on various information. Compared with the BiLSTM model, the model in this paper considers information from multiple angles, the overall recognition effect of the model is also better than other models of the same level, and the F1-Score reaches 94.47%.