Transformer-Based Temporal Information Extraction and Application: A Review

2025-04-10

AI Summary

This paper addresses the absence of a systematic survey on Transformer-based approaches for temporal information extraction (Temporal IE). It presents the first comprehensive review of over 50 key works published between 2018 and 2024. Methodologically, it establishes a unified taxonomy analyzing models along three dimensions: modeling paradigms (e.g., joint event-temporal modeling, temporal token enhancement), task adaptation strategies (e.g., prompt-based fine-tuning, multi-task learning), and evaluation frameworks—covering domains including healthcare, news, and intelligence analysis. The study identifies critical bottlenecks, notably poor cross-domain transferability and weak long-horizon temporal reasoning. It further proposes three scalable future directions: lightweight temporal encoding, structured prompt design, and causal temporal modeling. Collectively, this work provides both theoretical foundations and practical guidelines for enhancing Transformers’ capacity to understand and robustly reason over temporal structures.

Technology Category

• Natural Language Processing: Information Extraction
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Extracting structured temporal data from unstructured text
• Applying Transformer models to improve temporal information extraction
• Reviewing advancements and future directions in temporal IE

Innovation

• Transformer-based models for temporal information extraction
• Pre-trained language models enhancing temporal reasoning
• Systematic review of temporal IE with Transformers