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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhao, Feng Zhang, Zhilu Yan, Cheng Liu, Xianggan Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798891761964

In knowledge graphs (KGs), predicting missing relations is a critical reasoning task. Recent subgraph-based models have delved into inductive settings, which aim to predict relations between newly added entities. While these models have demonstrated the ability for inductive reasoning, they only consider the structural information of the subgraph and neglect the loss of semantic information caused by replacing entities with nodes. To address this problem, we propose a novel Commonsense Subgraph Meta-Learning (CSML) model. Specifically, we extract concepts from entities, which can be viewed as high-level semantic information. Unlike previous methods, we use concepts instead of nodes to construct commonsense subgraphs. By combining these with structural subgraphs, we can leverage both structural and semantic information for more comprehensive and rational predictions. Furthermore, we regard concepts as meta-information and employ meta-learning to facilitate rapid knowledge transfer, thus addressing more complex few-shot scenarios. Experimental results confirm the superior performance of our model in both standard and few-shot inductive reasoning. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Correcting language Model Bias for Text Classification in True Zero-Shot Learning 30

Correcting Language Model Bias for Text Classification in Tr...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on language Resources and Evaluation, LREC-COLING 2024

作者： Zhao, Feng Wan, Xianlin Yan, Cheng Loo, Chu Kiong Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Huazhong University of Science and Technology China Faculty of Computer Science & Information Technology University of Malaya Malaysia

ISBN: (纸本)9782493814104

Combining pre-trained language models (PLMs) and manual templates is a common practice for text classification in zero-shot scenarios. However, the effect of this approach is highly volatile, ranging from random guesses to near state-of-the-art results, depending on the quality of the manual templates. In this paper, we show that this instability stems from the fact that language models tend toward predicting certain label words of text classification, and manual templates can influence this tendency. To address this, we develop a novel pipeline for annotating and filtering a few examples from unlabeled examples. Moreover, we propose a new method to measure model bias on label words that utilizes unlabeled examples as a validation set when tuning language models. Our approach does not require any pre-labeled examples. Experimental results on six text classification tasks demonstrate that the proposed approach significantly outperforms standard prompt learning in zero-shot settings, achieving up to 19.7% absolute improvement and 13.8% average improvement. More surprisingly, on IMDB and SST-2, our approach even exceeds all few-shot baselines. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Zero-shot learning

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iHofman: a predictive model integrating high-order and low-order features with weighted attention mechanisms for circRNA-miRNA interactions

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BMC Biology 2025年第1期23卷 1-19页

作者： Yu, Chang-Qing Jiang, Chen Wang, Lei You, Zhu-Hong Wang, Xin-Fei Wei, Meng-Meng Shi, Tai-Long Liang, Si-Zhe School of Information Engineering Xijing Univerity Xi’an 710123 China Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Guangxi Academy of Science Nanning 530007 China School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China School of Computer Science Northwestern Polytechnical University Xi’an 710129 China College of Computer Science and Technology Jilin University Changchun 130012 China

Background: Increasing research indicates that the complex interactions between circular RNAs (circRNAs) and microRNAs (miRNAs) are critical for diagnosing and treating various human diseases. Consequently, accurately predicting potential circRNA-miRNA interactions (CMIs) has become increasingly important and urgent. Traditional biological experiments, however, are often labor-intensive, time-consuming, and prone to external influences. Results: To tackle this challenge, we present a novel model, iHofman, designed to predict CMIs by integrating high-order and low-order features with weighted attention mechanisms. Specifically, we first extract sequence and structural information representations using FastText and GraRep, respectively, and capture high-order and low-order features from sequence information representations using stacked autoencoders. Subsequently, weighted attention mechanisms are applied for feature fusion, focusing on the most relevant information. Finally, multi-layer perceptron is employed to accurately infer potential CMIs. In the fivefold cross-validation (CV) experiment on the baseline dataset, iHofman achieved an accuracy of 82.49% with an AUC of 0.9092. iHofman also demonstrates solid performance on other CMI datasets. In case studies, 26 of the top 30 CMIs with the highest iHofman predictive scores were confirmed in relevant literature. Conclusions: The above experimental results indicate that iHofman can effectively predict potential CMIs and has achieved outstanding performance compared with existing methods. It provides a reliable supplementary approach for subsequent biological wet experiments. © The Author(s) 2025.

关键词： CircRNA CircRNA-miRNA interaction FastText GraRep MiRNA Weighted attention mechanisms

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DySarl: Dynamic Structure-Aware Representation Learning for Multimodal Knowledge Graph Reasoning 24

DySarl: Dynamic Structure-Aware Representation Learning for ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Kangzheng Zhao, Feng Yang, Yu Xu, Guandong School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China University of Technology Sydney Sydney Australia Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China The Hong Kong Polytechnic University Hong Kong Hong Kong The Education University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798400706868

Multimodal knowledge graph (MKG) reasoning has attracted significant attention since impressive performance has been achieved by adding multimodal auxiliary information (i.e., texts and images) to the entities of traditional KGs. However, existing studies heavily rely on path-based methods for learning structural modality, failing to capture the complex structural interactions among multimodal entities beyond the reasoning path. In addition, existing studies have largely ignored the dynamic impact of different multimodal features on different decision facts for reasoning, which utilize asymmetric coattention to independently learn the static interplay between different modalities without dynamically joining the reasoning process. We propose a novel Dynamic Structure-aware representation learning method, namely DySarl, to overcome this problem and significantly improve the MKG reasoning performance. Specifically, we devise a dual-space multihop structural learning module in DySarl, aggregating the multihop structural features of multimodal entities via a novel message-passing mechanism. It integrates the message paradigms in Euclidean and hyperbolic spaces, effectively preserving the neighborhood information beyond the limited multimodal query paths. Furthermore, DySarl has an interactive symmetric attention module to explicitly learn the dynamic impacts of unimodal attention senders and multimodal attention targets on decision facts through a newly designed symmetric attention component and fact-specific gated attention unit, equipping DySarl with the dynamic associations between the multimodal feature learning and later reasoning. Extensive experiments show that DySarl achieves significantly improved reasoning performance on two public MKG datasets compared with that of the state-of-the-art baselines. Source codes are available at https://***/HUSTNLP-codes/DySarl. © 2024 ACM.

关键词： Knowledge graph

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On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4439-4451页

作者： Bucur, Ana-Maria Moldovan, Andreea-Codrina Parvatikar, Krutika Zampieri, Marcos KhudaBukhsh, Ashiqur R. Dinu, Liviu P. University of Bucharest Interdisciplinary School of Doctoral Studies Bucharest050663 Romania Universitat Politècnica de València PRHLT Research Center València46020 Spain Rochester Institute of Technology RochesterNY14623 United States George Mason University FairfaxVA22030 United States University of Bucharest Human Language Technology Research Center Department of Computer Science Faculty of Mathematics and Computer Science Bucharest050663 Romania

Computational approaches to predicting mental health conditions in social media have been substantially explored in the past years. Multiple reviews have been published on this topic, providing the community with comprehensive accounts of the research in this area. Among all mental health conditions, depression is the most widely studied due to its worldwide prevalence. The COVID-19 global pandemic, starting in early 2020, has had a great impact on mental health worldwide. Harsh measures employed by governments to slow the spread of the virus (e.g., lockdowns) and the subsequent economic downturn experienced in many countries have significantly impacted people's lives and mental health. Studies have shown a substantial increase of above 50% in the rate of depression in the population. In this context, we present a review on natural language processing (NLP) approaches to modeling depression in social media, providing the reader with a post-COVID-19 outlook. This review contributes to the understanding of the impacts of the pandemic on modeling depression in social media. We outline how state-of-the-art approaches and new datasets have been used in the context of the COVID-19 pandemic. Finally, we also discuss ethical issues in collecting and processing mental health data, considering fairness, accountability, and ethics. © 2013 IEEE.

关键词： COVID-19

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Vision-Based Multimodal Interfaces: A Survey and Taxonomy for Enhanced Context-Aware System Design 25

Vision-Based Multimodal Interfaces: A Survey and Taxonomy fo...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Hu, Yongquan Gowen' Tang, Jingyu Gong, Xinya Zhou, Zhongyi Zhang, Shuning Elvitigala, Don Samitha Mueller, Florian Gfloyd' Hu, Wen Quigley, Aaron J. School of Computer Science and Engineering University of New South Wales Sydney Australia School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The University of Tokyo Tokyo Japan Tsinghua University Beijing China Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia CSIRO's Data61 Sydney Australia University of New South Wales Sydney Australia

ISBN: (纸本)9798400713941

The recent surge in artificial intelligence, particularly in multimodal processing technology, has advanced human-computer interaction, by altering how intelligent systems perceive, understand, and respond to contextual information (i.e., context awareness). Despite such advancements, there is a significant gap in comprehensive reviews examining these advances, especially from a multimodal data perspective, which is crucial for refining system design. This paper addresses a key aspect of this gap by conducting a systematic survey of data modality-driven Vision-based Multimodal Interfaces (VMIs). VMIs are essential for integrating multimodal data, enabling more precise interpretation of user intentions and complex interactions across physical and digital environments. Unlike previous task- or scenario-driven surveys, this study highlights the critical role of the visual modality in processing contextual information and facilitating multimodal interaction. Adopting a design framework moving from the whole to the details and back, it classifies VMIs across dimensions, providing insights for developing effective, context-aware systems. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： human computer interaction

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Causal Graph Representation Learning for Outcome-Oriented Link Prediction

Causal Graph Representation Learning for Outcome-Oriented Li...

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International Joint Conference on Neural Networks (IJCNN)

作者： Langjunqing Jin Feng Zhao Cheng Yan Xiangyu Gui School of Computer Science and Technology Natural Language Processing and Knowledge Graph Lab Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Causal graph representation learning has been an essential tool for improving the performance achieved in downstream tasks such as link prediction in causal graphs. Most of the existing methods can learn the links between binary nodes in an observed graph structure. However, the causal relations between nodes and their multivariate continuous nature are easily overlooked when learning knowledge graph links. This can lead to difficulty when attempting to find the real causes affecting the facts in multiple node chains, thus reducing the accuracy of prediction. Therefore, we propose a causal representation learning model for link prediction (CRLLP) to accurately complete missing links in causal graphs. Treating the node embeddings in causal graphs as contexts is a novel paradigm for characterizing causal relations in which the exsiting graph structure are treatments and the links to be completed are outcomes. In addition, we propose an outcome-oriented counterfactual link prediction method that learns causal chain representations based on observed nodes and queries the causal links in causal graphs by answering counterfactual questions. The developed method can clearly identify the causes corresponding to different outcomes. The experimental results show that our model can achieve optimal link prediction and causal chain inference performance with highly accurate causal characterization representations. Furthermore, other experiments demonstrate the superiority and interpretability of our model.

关键词： Representation learning Accuracy Neural networks Knowledge graphs Predictive models Benchmark testing

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Towards Information Sharing Beetle Antennae Search Optimization 24th

Towards Information Sharing Beetle Antennae Search Optimizat...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Liu, Xuan Wang, Chenyan Liu, Wenjian Zhang, Lefeng Liu, Xianggan Gao, Yutong Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of China Beijing China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan China City University of Macau China Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789819615278

The study of bioinformatics-based evolutionary computation has long been of significant interest within the scientific community. Beetle antennae search algorithm is widely used because of its lightweight, however, it lacks information sharing due to individual iteration in the algorithm. In this paper, we propose an innovative pheromone-based beetle antennae search algorithm, which evolves from a single iterative individual in BAS algorithm to multiple parallel iterative individuals and incorporates the pheromone sharing mechanism found in ant colony optimization algorithm. Applying the pheromone-based beetle antennae search algorithm to the virtual machine placement problem in cloud computing, we find that pheromone sharing mechanism allows the PB-BAS algorithm to exhibit superior optimization capabilities and effectively avoids convergence to local optimal. To verify the performance of the algorithm, we select other alternative algorithms and conduct a large number of comparative experiments under different experimental setups, the experimental results show the effectiveness and efficiency of our algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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Improving the Interpretability Through Maximizing Mutual Information for EEG Emotion Recognition

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IEEE Transactions on Affective Computing 2025年第2期16卷 744-757页

作者： Yang, Hua Chen, C. L. Philip Chen, Bianna Zhang, Tong North Carolina State University Department of Computer Science RaleighNC27695 United States South China University of Technology Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition The School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou510335 China

Trustworthy Graph Neural Networks (GNNs) for EEG emotion recognition should identify emotions accurately and elucidate corresponding rationales. Current GNNs have achieved notable performance by dynamically modeling emotional connections between EEG channels. However, these GNNs lack interpretability due to the absence of explicit rationale behind their predictions. This paper conducts a comprehensive identification of important EEG channels to enhance the interpretability of EEG emotion recognition from the perspective of mutual information. Specifically, an Adjacency-Explainable Graph Neural Network (AEG) for ante-hoc interpretability is proposed to capture genuine EEG emotional connections, which gives a theoretical guarantee to remove spurious connections. Moreover, a Channel-wise Adaptive Class Activation Mapping Explainer (CACA) for post-hoc interpretability is developed to locate the EEG channels that contribute most to predictions. Experimental results on three datasets, i.e., SEED, SEED-IV, and DREAMER, prove that imbuing training processes with enhanced interpretability ensures significant performance improvements in emotion recognition. Quantitative comparisons of post-hoc interpretability also demonstrate the superiority of CACA. Furthermore, this paper illustrates two potential applications of the proposed methodologies, showing their broader utility and significance. © 2010-2012 IEEE.

关键词： Graph neural networks

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Look one step ahead through first-order aggregation in reinforcement learning-based knowledge graph reasoning

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Information sciences 2025年 718卷

作者： Wang, Hao Song, Dandan Wu, Zhijing Tian, YuHang Xu, Jing Laboratory of High-Volume Language Information Processing and Cloud Computing Beijing Lab of Intelligent Information Technology School of Computer Science and Technology Beijing Institute of Technology Beijing Beijing100081 China

Multi-hop reasoning is an effective and interpretable approach for query answering, as it finds reasoning paths over knowledge graphs (KGs) to enhance interpretability. Recent studies have applied reinforcement learning-based (RL-based) methods with policy agents to solve this problem. However, these methods primarily focus on sequential reasoning paths and candidate nodes, while overlooking the local structural information of adjacency subgraphs and the semantic correlations between relations at each decision step. In this paper, we propose a novel RL-based multi-hop reasoning model, Look One Step Ahead (LOSA), which leverages first-order aggregation to regard more expressive adjacency subgraphs rather than nodes as candidate actions and pays attention to the semantic correlations between relations. Specifically, an adjacency aggregation module encodes the local subgraph information and feeds the representations into the policy network to guide decision-making, thereby reducing backtracking and mitigating sparse-reward issues. Furthermore, a semantic matching module is designed to emphasize the semantic correlations of relations to improve the rationality of the reasoning paths. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed approach. © 2025

关键词： Reinforcement learning

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