Aspect Sentiment Quad Prediction (ASQP) aims to extract all sentiment elements in quads for a given review to explain the reason for the sentiment. Previous table-filling based methods have achieved promising results by modeling word-pair relations. However, these methods decompose the ASQP task into several subtasks without considering the association between sentiment elements. Most importantly, they fail to tackle the situation where a sentence contains multiple implicit expressions. To address these limitations, we propose a simple yet effective Unified Grid Tagging Scheme (UGTS) to extract sentiment quadruplets in one shot, with two additional special tokens from pre-trained models to represent potential implicit aspect and opinion terms. Based on this, we first introduce the adaptive graph diffusion convolution network to construct the direct connection between explicit and implicit sentiment elements from syntactic and semantic views. Next, we utilize conditional layer normalization to refine the mutual indication effect between words for matching valid aspect-opinion pairs. Finally, we employ the triaffine mechanism to integrate heterogeneous word-pair relations to capture higher-order interactions between sentiment elements. Experimental results on four benchmark datasets show the effectiveness and robustness of our model, which achieves state-of-the-art performance.

Chinese idioms pose a significant challenge for machine reading comprehension due to their metaphorical meanings often diverging from their literal counterparts, leading to metaphorical inconsistency. Furthermore, the same idiom can have different meanings in different contexts, resulting in contextual inconsistency. Although deep learning-based methods have achieved some success in idioms reading comprehension, existing approaches still struggle to accurately capture idiom representations due to metaphorical inconsistency and contextual inconsistency of idioms. To address these challenges, we propose a novel model, Multi-Semantic Contrastive Learning Method (MSCLM), which simultaneously addresses metaphorical inconsistency and contextual inconsistency of idioms. To mitigate metaphorical inconsistency, we propose a metaphor contrastive learning module based on the prompt method, bridging the semantic gap between literal and metaphorical meanings of idioms. To mitigate contextual inconsistency, we propose a multi-semantic cross-attention module to explore semantic features between different metaphors of the same idiom in various contexts. Our model has been compared with multiple current latest models (including GPT-3.5) on multiple Chinese idiom reading comprehension datasets, and the experimental results demonstrate that MSCLM outperforms state-of-the-art models.

Chinese idioms on social media demand a nuanced understanding for correct usage. The Chinese idiom cloze test poses a unique challenge for machine reading comprehension due to the figurative meanings of idioms deviating from their literal interpretations, resulting in a semantic bias in models’ comprehension of idioms. Furthermore, given that the figurative meanings of many idioms are similar, their use as suboptimal options can interfere with optimal selection. Despite achieving some success in the Chinese idiom cloze test, existing methods based on deep learning still struggle to comprehensively grasp idiom semantics due to the aforementioned issues. To tackle these challenges, we introduce a Refining Idioms Semantics Comprehension Framework (RISCF) to capture the comprehensive idioms semantics. Specifically, we propose a semantic sense contrastive learning module to enhance the representation of idiom semantics, diminishing the semantic bias between figurative and literal meanings of idioms. Meanwhile, we propose an interference-resistant cross-attention module to attenuate the interference of suboptimal options, which considers the interaction between the candidate idioms and the blank space in the context. Experimental results on the benchmark datasets demonstrate the effectiveness of our RISCF model, which outperforms state-of-the-art methods significantly.

Fact-checking fake news detection involves using verified accurate factual information in news reports as "evidence" to validate objective statement "claim". Existing works primarily focus on identifying critical elements within the evidence that support or refute specific claims by assessing the congruence or divergence between the claim and the associated evidence. These methods can broadly be divided into text-based and graph-based-the former centers on understanding the nuances of unstructured text to extract semantic word-level information. At the same time, the latter is proficient at analyzing the node-level structure of graphs it creates from the text to reveal topological insights. Each type provides a distinct view on identifying critical elements for fact-checking. To enhance the complementary nature of the two perspectives, this paper proposes an end-to-end framework for fact-checking fake news detection entitled Multi-view Counterfactual Contrastive Learning (MCCL). The framework incorporates a counterfactual technique to refine the fused features from both the "entity-view" of textual content and the "centrality-view" of the graph structure. Additionally, it employs contrastive learning to sharpen the distinctions among multi-view features, which facilitates the exact identification of critical elements in the evidence related to their respective claims. Experimental results on real datasets demonstrate that the proposed MCCL outperforms state-of-the-art methods.

Discovering new therapeutic indications for existing drugs is the essential part of drug repurposing. As a safe, low-cost and time-saving drug discovery technique, drug repurposing is attracting more and more research attention. However, there are still many potential drug-disease therapeutic effects uncovered in biological data sources. The advance of computation techniques offers great support for drug repurposing. In this study, we propose a computational approach, named BiGNN, to explore potential drug-disease associations. First, we collect multiple biological data sources to construct the heterogeneous information networks. Then BiGNN employs a bilevel graph-based neural network frameworks to aggregating features via node-level embedding and graph-level embedding respectively. Finally, the aggregated features are used to identify drug-disease associations. The experiment results demonstrate that BiGNN achieves outperforming performance with the average AUPROC of 0.95S and AUPR of 0.647 when evaluating on two benchmark datasets by 10-fold cross-validation.