Target and Precursor Named Entities Recognition from Scientific Texts of High-Temperature Steel Using Deep Neural Network.

Named Entity Recognition (NER) is an essential task in natural language processing, especially in the domain of scientific texts. This paper presents a study of NER for scientific texts in high-temperature steel, a type of alloy used in various applications where high temperatures prevail. We propose a NER system using Bi-LSTM with a domain-specific embedding approach and evaluate its performance on a test dataset. The study results show that the proposed NER system achieves an F1 score of 0.99, indicating that it can accurately identify and classify named entities in scientific texts about high-temperature steel with high precision and recall. The proposed approach was more effective than the classical machine learning-based approach. Our results suggest that the domain-specific embedded Bi-LSTM technique can be an effective approach for NER in scientific texts, especially in specialized domains such as high-temperature steel.