Understanding and analyzing event temporal relations is a crucial task in Natural Language Processing (NLP). This task, known as Event Temporal Relation Extraction (ETRE), aims to identify and extract temporal connections between events in text. Recent studies focus on locating the relative position of event pairs on the timeline by designing logical expressions or auxiliary tasks to predict their temporal occurrence. Despite these advances, this modeling approach neglects the multidimensional information in temporal relation and the hierarchical process of reasoning. In this study, we propose a novel hierarchical modeling approach for this task by introducing a Temporal Cognitive Tree (TCT) that mimics human logical reasoning. Additionally, we also design a integrated model incorporating prompt optimization and deductive reasoning to exploit multidimensional supervised information. Extensive experiments on TB-Dense and MATRES datasets demonstrate that our approach outperforms existing methods.

Event representation learning plays a crucial role in numerous natural language processing (NLP) tasks, as it facilitates the extraction of semantic features associated with events. Current methods of learning event representation based on contrastive learning processes positive examples with single-grain random masked language model (MLM), but fall short in learn information inside events from multiple aspects. In this paper, we introduce multi-grained contrastive learning and triple-mixture of experts (MCTM) for event representation learning. Our proposed method extends the random MLM by incorporating a specialized MLM designed to capture different grammatical structures within events, which allows the model to learn token-level knowledge from multiple perspectives. Furthermore, we have observed that mask tokens with different granularities affect the model differently, therefore, we incorporate mixture of experts (MoE) to learn importance weights associated with different granularities. Our experiments demonstrate that MCTM outperforms other baselines in tasks such as hard similarity and transitive sentence similarity, highlighting the superiority of our method.

Few-shot Event Detection (FSED) is a meaningful task due to the limited labeled data and expensive manual labeling. Some prompt-based methods are used in FSED. However, these methods require large GPU memory due to the increased length of input tokens caused by concatenating prompts, as well as additional human effort for designing verbalizers. Moreover, they ignore instance and prompt biases arising from the confounding effects between prompts and texts. In this paper, we propose a prototype-based prompt-instance Interaction with causal Intervention (2xInter) model to conveniently utilize both prompts and verbalizers and effectively eliminate all biases. Specifically, 2xInter first presents a Prototype-based Prompt-Instance Interaction (PPII) module that applies an interactive approach for texts and prompts to reduce memory and regards class prototypes as verbalizers to avoid design costs. Next, 2xInter constructs a Structural Causal Model (SCM) to explain instance and prompt biases and designs a Double-View Causal Intervention (DVCI) module to eliminate these biases. Due to limited supervised information, DVCI devises a generation-based prompt adjustment for instance intervention and a Siamese network-based instance contrasting for prompt intervention. Finally, the experimental results show that 2xInter achieves state-of-the-art performance on RAMS and ACE datasets.

“基于电子病历构建医学知识图谱对医疗技术的发展具有重要意义,实体和关系抽取是构建知识图谱的关键技术。本文针对目前实体关系联合抽取中存在的特征交互不充分的问题,提出了一种平行交互注意力网络(PIAN)以充分挖掘实体与关系的相关性,在多个标准的医学和通用数据集上取得最优结果;当前中文医学实体及关系标注数据集较少,本文基于中文电子病历构建了实体和关系抽取数据集(CEMRIE),与医学专家共同制定了语料标注规范,并基于所提出的模型实验得出基准结果。”