综述基于Transformer的时序信息抽取方法及其应用前景
Transformer-Based Temporal Information Extraction and Application: A Review
- 系统梳理Transformer在时序信息抽取中的应用思路
- 总结多领域（医疗、新闻、情报）中时序建模的性能表现
- 适合关注时序理解与NLP前沿的研究者参考
时序信息抽取（Temporal IE）旨在从非结构化文本中提取结构化的时序信息，揭示其中隐含的时间线。该技术广泛应用于医疗、新闻报道和情报分析等领域，帮助模型进行时序推理，并辅助人类理解文本的时间脉络。基于Transformer的预训练语言模型在自然语言处理中取得了革命性进展，在多项任务中表现出色。尽管基于Transformer的方法在时序信息抽取方面已取得显著成果，但相关系统性综述仍较为缺乏。本文旨在填补这一空白，系统总结并分析近年来基于Transformer的时序信息抽取研究工作，同时指出未来可能的研究方向。
原文摘要 · Abstract (English)
Temporal information extraction (IE) aims to extract structured temporal information from unstructured text, thereby uncovering the implicit timelines within. This technique is applied across domains such as healthcare, newswire, and intelligence analysis, aiding models in these areas to perform temporal reasoning and enabling human users to grasp the temporal structure of text. Transformer-based pre-trained language models have produced revolutionary advancements in natural language processing, demonstrating exceptional performance across a multitude of tasks. Despite the achievements garnered by Transformer-based approaches in temporal IE, there is a lack of comprehensive reviews on these endeavors. In this paper, we aim to bridge this gap by systematically summarizing and analyzing the body of work on temporal IE using Transformers while highlighting potential future research directions.
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