Event Causality Identification (ECI) is a critical and challenging Natural Language Processing (NLP) task. Despite Large Language Models (LLMs) offer the potential for ground-breaking achievements through In-Context Learning (ICL), they still demonstrate significant weaknesses in ECI, as evidenced by series causal hallucinations. We argue that this is due to three major shortcomings of ICL for ECI: (1) conventional retrieval methods fail to ensure sufficient causal similarity between input queries and ICL demonstrations; (2) the lack of explanations of causal relationships in demonstrations leading to poor ICL effectiveness; and (3) limited non-causal knowledge in LLMs causing a misalignment between ICL and human causal cognition. In this paper, we propose CarICL to address the aforementioned issues by: (1) incorporating causality-aware representations in demonstration retrieval; (2) enriching the demonstrations with cause-and-effect reasoning; and (3) aligning LLMs with human causal cognition through causality preference optimization. Experimental results show that CarICL outperforms state-of-the-art baselines on three widely used sentence-level ECI benchmarks.

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.

Biomedical causal relation extraction (BCRE) as a sub-task of biomedical information extraction aims to extract event causal relation facts from unstructured biomedical texts and plays an important role in some downstream tasks. The existing methods usually apply oversampling to solve the problems caused by the unbalanced distribution and limited knowledge of the datases, which may ignore the sample diversity. In addition, they usually encode the text by the pre-trained language model BioBERT, which can only obtain context information of the text and may limit the performance because of the insufficiently extracted text information. To solve the above mentioned problems, in this paper, we propose a Multi-source Knowledge Fusion Network (MKFN) to augment data as well as sufficiently extract and fuse the text information and the external knowledge for biomedical causal relation extraction. Specifically, we apply the large language model Roberta to augment samples in minority classes and filter the external knowledge from multiple knowledge bases with the relevance of the text to triples captured by the structure information. Afterward, the multi-source knowledge embedding including context information, structure information and the corresponding external knowledge is acquired by various different encoders. Additionally, we utilize the triplet attention, which is introduced into event relation extraction for the first time, to fuse the multi-source knowledge embedding. Extensive experimental results on Hahn-Powell’s and BioCause datasets confirm that the proposed method achieves novel state-of-the-art performance compared with the current advances.

Medical events, such as diagnostic events, treatment events and examination events, and the relationships between these medical events are of importance in medical research. Moreover, the temporal relationship is one of the most basic medical event relationships. However, people often cannot clearly obtain the relationship between medical events directly from electronic medical records. This defect can lead to inconvenient query construction, inefficient query execution, and poor query performance when queries need to be made for certain diseases or symptoms. In this paper, we propose a method to construct a time-guided medical event chain, which combines medical events of patients with the same disease or symptoms according to the temporal relationship. We use BERT and Bi-LSTM joint encoding to learn contextual information to detect medical event trigger words, and test the effectiveness of our model on MLEE dataset. Finally, we extract medical event chains on the CCKS2020 evaluation dataset. Through the event chain, patients and doctors can more easily understand the trend of the disease and analyze the disease and the treatment effect, and it is helpful for some complex clinical research. Moreover, medical event chain can be easily integrated into existing medical knowledge graph (such as CMeKG) for auxiliary diagnosis.

Routing is one of the most challenging problems in opportunistic networks (OppNets) because of the intermittence of the network connection. To address the issue, many routing schemes have been proposed, however, most of them assume that nodes are willing to forward messages for others. In fact, due to limited resources and poor social relations, nodes in OppNets may be selfish and not reluctant to participate in message forwarding. To tackle this issue, in this paper, we propose a Probabilistic Routing scheme based on Game Theory (PRGT) to stimulate cooperation among selfish nodes. Firstly, we introduce virtual money to buy the message for gaining more profits. Then, according to the historical meeting records among different nodes, we establish a Markov-based probability prediction model, in which the message carrier selects a node with higher probability of encountering the destination node as the relay node. Finally, a game theory approach is employed to simulate trading price for message forwarding. The simulation results demonstrate that our proposed routing scheme can effectively improve the delivery rate of messages and reduce network latency.