Oral history research and language technology innovation in the era of artificial intelligence

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1. Traditional Methods and Dilemmas of Oral History Research

Traditional oral history research mainly relies on manual interviews, recording and compilation. Interviewers obtain precious historical memories and personal experiences through face-to-face communication with interviewees. However, this method has many limitations. First, manual interviews are relatively inefficient and require a lot of time and energy. Secondly, in the process of recording and collating, information may be distorted due to human negligence or misunderstanding. In addition, for large-scale oral history projects, how to effectively manage and retrieve massive amounts of data is also a difficult problem.

2. The rise and application of natural language processing technology

With the development of artificial intelligence technology, natural language processing technology has gradually come into people's view. Natural language processing aims to enable computers to understand and process human natural language, including speech recognition, text generation, machine translation and other fields. These technologies have brought new possibilities to oral history research. For example, speech recognition technology can quickly convert oral content into text, greatly improving the efficiency of recording. At the same time, text generation technology can automatically generate summaries or summaries based on existing oral materials, helping researchers grasp key information more quickly. Machine translation technology can break down language barriers, allowing oral materials from different language backgrounds to be more widely understood and studied.

3. Advantages and Challenges of Natural Language Processing Technology in Oral History Research

The application of natural language processing technology in oral history research has obvious advantages. On the one hand, it improves the efficiency and accuracy of research. Through automated processing procedures, human errors can be reduced, and a large amount of oral materials can be processed quickly. On the other hand, it expands the scope and depth of research. Using machine translation technology, researchers can access more oral history resources from different language backgrounds, thereby enriching the perspective and content of research. However, natural language processing technology also faces some challenges in oral history research. First, the accuracy and reliability of the technology still need to be improved. Although speech recognition and text generation technology have made great progress, errors may still occur when faced with complex language environments and accents. Secondly, the application of technology needs to take into account ethical and legal issues. For example, the use and dissemination of oral materials need to comply with relevant laws, regulations and ethical standards to protect the privacy and rights of interviewees.

IV. Future Prospects and Response Strategies

Faced with the opportunities and challenges brought by natural language processing technology, oral history researchers need to actively respond and explore innovative research methods and strategies. In terms of technology, we should continuously improve and optimize natural language processing technology to improve its accuracy and adaptability. At the same time, we should strengthen interdisciplinary cooperation, integrate knowledge and resources from multiple fields such as computer science, linguistics, and history, and jointly promote the development of oral history research. In terms of ethics and law, we should establish and improve relevant norms and systems to ensure the legal use and dissemination of oral materials. In addition, we should strengthen the training and education of researchers, improve their technical literacy and ethical awareness, and ensure that the correct principles and methods are followed in the research process. In short, natural language processing technology has brought new vitality and opportunities to oral history research, but we also need to be cautious, give full play to its advantages, overcome its challenges, and promote oral history research to achieve more fruitful results in the era of artificial intelligence.