# Construction of a Geological Fault Corpus and Named Entity Recognition
## 6 Conclusions and Discussion  
In this paper, the author provides a discussion of the current development status of named entity recognition in the geological domain, and conducts research on the dearth of fault corpora and the suboptimal accuracy of named entity recognition models. The author and others constructed a small-scale, fine-grained fault corpus and proposed a fault entity recognition model based on it. The model developed in this paper integrates the BERT pre-training model, bi-directional long and short-term memory network (BiLSTM), and conditional random field (CRF). The corresponding word vectors are obtained by using the BERT pre-training model, the sequence characteristics are learnt by the BiLSTM layer, the CRF layer encodes the output of the BiLSTM layer to obtain the predicted labelled sequences, and finally, the individual entities are extracted and classified. In order to fully demonstrate the applicability of the proposed model, three sets of comparison experiments are setup in this paper. The findings of these experiments demonstrate the superiority of the proposed BERT-BiLSTM-CRF model in the identification of fault entities when compared to the BiLSTM-CRF model. The model attains 82.54% precision, 84.23% recall, and 84.07% F1 value. The experimental outcomes substantiate the model's enhanced performance in the realm of fault entity recognition. However, despite its outstanding performance in the comparison experiments between the model sex and other models, the model shows some limitations in the recognition accuracy of geographic location, water system geomorphology and seismic hazards due to the small size and quality of the dataset. This limitation is closely related to the size and quality of the corpus. A smaller corpus will result in the model failing to capture domain-specific semantics and contextual \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Training Time (s)** \\ \hline BiLSTM-CRF & 336 \\ \hline BERT-BiLSTM-CRF & 1130 \\ \hline ALBERT-BiLSTM-CRF & 804 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Training time for different models. relationships during training. Furthermore, while BERT demonstrates efficacy in multiple Natural Language Processing (NLP) tasks, its applicability in the geological domain remains to be substantiated. The pre-training of the BERT model is informed by a substantial general-purpose corpus, which can lead to erroneous recognition of domain-specific entities when confronted with specialized geological data. The contributions of this study can be summarized as follows. From a theoretical standpoint, this study employs deep learning models within the domain of geology, with the selected BERT-BiLSTM-CRF model demonstrating superior performance in the extraction of fault entities when compared to alternative models. The study not only offers novel concepts for the recognition of named entities within the field of geology, but also provides a foundation for the advancement of named entity recognition in related disciplines. From an applied perspective, the creation of a small-scale, fine-grained fault corpus by the study is of particular significance, as it successfully addresses a critical gap in the construction of such corpora. The corpus establishes the basis for subsequent research and applications, and provides significant data support for the task of named entity recognition in the field of geology. Future research will focus on the following three aspects: (a) the construction of fault corpus is a continuous optimization process. The authors will further enrich and improve the fault entity annotation corpus, expand the fault entity classification, and gradually form a high-quality, large-scale fault corpus; (b) optimize the structure of the existing model, and explore the application of the model with different parameter configurations in the fault entity recognition task, with a view to obtaining better recognition results in the fault entity recognition task.