Entity linking (EL) focuses on accurately associating ambiguous mentions in text with corresponding entities in a knowledge graph. Traditional methods mainly rely on fine-tuning or training on specific datasets. However, they suffer from insufficient semantic comprehension, high training costs, and poor scalability. Large Language Models (LLMs) offer promising solutions for EL, but face key challenges: weak simple-prompt performance, costly fine-tuning, and limited recall and precision due to the lack of LLMs use in candidate generation. Building on this, we introduce a novel framework: **A**daptive **E**ntity **L**inking with LLM-Driven **C**ontextualization. AELC, for the first time, introduces the combination of high-density key information condensation prompt and tool-invocation strategy, using a unified format semantic filtering strategy and an adaptive iterative retrieval mechanism to dynamically optimize the candidate set, significantly enhancing both precision and coverage. Furthermore, we innovatively reformulate the EL task as a multiple-choice problem, enabling multi-round reasoning to substantially improve the model’s discriminative capability and robustness. Experiments on four public benchmark datasets demonstrate that AELC achieves state-of-the-art performance. Further ablation studies validate the effectiveness of each module.

Extracting event causality underlies a broad spectrum of natural language processing applications. Cutting-edge methods break this task into Event Detection and Event Causality Identification. Although the pipelined solutions succeed in achieving acceptable results, the inherent nature of separating the task incurs limitations. On the one hand, it suffers from the lack of cross-task dependencies and may cause error propagation. On the other hand, it predicts events and relations separately, undermining the integrity of the event causality graph (ECG). To address such issues, in this paper, we propose an approach for Structure-Aware Event Causality Generation (SEAG). With a graph linearization module, we generate the ECG structure in a way of text2text generation based on a pre-trained language model. To foster the structural representation of the ECG, we introduce the novel Causality Structural Discrimination training paradigm in which we perform structural discriminative training alongside auto-regressive generation enabling the model to distinguish from constructed incorrect ECGs. We conduct experiments on three datasets. The experimental results demonstrate the effectiveness of structural event causality generation and the causality structural discrimination training.