古汉语NLP研究现状综述(2009-2024)OA
The Overview of Research Status for NLP Research in Ancient Chinese(2009-2024)
文章综述了古汉语自然语言处理(NLP)领域的研究现状,特别是下游任务方面的进展.通过分析 2009年至 2024 年的 23 篇相关论文,文章指出古汉语NLP面临的挑战,并探讨了包括断句与标点、分词、词性标注、命名实体识别等任务的研究方法和成果.研究发现,尽管古汉语与现代汉语在NLP任务上存在差异,但深度学习等技术的发展为古汉语文本处理提供了新途径.文章还讨论了多任务一体化研究的潜力,并对未来发展趋势进行了展望,强调了构建结构化数据集的重要性和对领域发展的促进作用.
This paper provides an overview of the current research status in the field of Natural Language Processing(NLP)of ancient Chinese,especially the progress in downstream tasks.By analyzing 23 relevant papers from 2009 to 2024,the paper points out the challenges faced by NLP in ancient Chinese and explores research methods and achievements including sentence breaks and punctuation,word segmentation,part of speech tagging,named entity recognition,and other tasks.The research finds that although there are differences in NLP tasks between ancient and modern Chinese,the development of technologies such as Deep Learning has provided new avenues for ancient Chinese text processing.This paper also discusses the potential of multi-task integration research and looks forward to future development trends,emphasizing the importance of constructing structured datasets and their promoting role in domain development.
劳斌;彭瑶;吕薇;植思喆
广东外语外贸大学 信息科学与技术学院,广东 广州 510006中山大学 外国语学院,广东 广州 510275
计算机与自动化
古汉语自然语言处理下游任务研究现状综述
ancient Chinesenatural language processingdownstream taskoverview of research status
《现代信息科技》 2024 (013)
146-150,155 / 6
广东省哲学社会科学规划项目(GD22CTS02);广东省基础与应用基础研究基金项目(2023A1515012817)
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