Search · four archives
Search · four archives
22 papers · ranked by Valyu relevance
Yinya Huang, Lemao Liu, Kun Xu, Meng Fang + 2 more
'Xiaodan Liang'] Abstract—Textual logical reasoning, especially question-answering (QA) tasks with logical reasoning, requires awareness of particular logical structures. The passage-level logical relations represent entailment or contradiction between propositional units (e.g., a concluding sentence). However, such…
Kong De, Nan Yu, Yun Yuan, Guohong Fu + 1 more
Emotion Cause Extraction in Conversations (ECEC) aims to extract the utterances which contain the emotional cause in conversations. Most prior research focuses on modelling conversational contexts with sequential encoding, ignoring the informative interactions between utterances and conversational-specific features for…
Zaid Almahmoud, Vibhor Agarwal, Rana Mahmoud, Nishanth Sastry
The proliferation of social media has made automated classification of online discourse, such as hate speech detection and polarity prediction, an essential task for maintaining digital safety and constructive discussions. However, online conversations are complex, context-dependent, and often structured in branching…
Areej Alhothali
Automatic Essay Scoring (AES) aims to evaluate the quality of written essays automatically, providing fast, consistent, and objective assessments of students' writing ability. Existing deep learning approaches-including recurrent, convolutional, and transformer-based models-primarily focus on textual semantics, yet…
Nicholas Botzer, Tim Weninger
Vast amounts of human communication occurs online. These digital traces of natural human communication along with recent advances in natural language processing technology provide for computational analysis of these discussions. In the study of social networks the typical perspective is to view users as nodes and…
Liang Li, Ruiying Geng, Bowen Li, Can Ma + 3 more
'Yongbin Li'] Most graph-to-text works are built on the encoder-decoder framework with crossattention mechanism. Recent studies have shown that explicitly modeling the input graph structure can significantly improve the performance. However, the vanilla structural encoder cannot capture all specialized information in a…
Wei Cui, Mingsheng Shang
Rumor posts have received substantial attention with the rapid development of online and social media platforms. The automatic detection of rumor from posts has emerged as a major concern for the general public, the government, and social media platforms. Most existing methods focus on the linguistic and semantic…
Hoang-Quynh Le, Duy-Cat Can, Nigel Collier
Background Most previous relation extraction (RE) studies have focused on intra sentence relations and have ignored relations that span sentences, i.e. inter sentence relations. Such relations connect entities at the document level rather than as relational facts in a single sentence. Extracting facts that are…
Zhao Chen, Danilo Comminiello, Tokunbo Ogunfunmi, Nithin V. George
Non-Euclidean data, such as social networks and citation relationships between documents, have node and structural information. The Graph Convolutional Network (GCN) can automatically learn node features and association information between nodes. The core ideology of the Graph Convolutional Network is to aggregate node…
Shuran Wang, Lilian Zhang, Lutao Gao, Yao Rao + 2 more
Gene regulatory network (GRN) inference is a central task in systems biology. However, due to the noisy nature of gene expression data and the diversity of regulatory structures, accurate GRN inference remains challenging. We hypothesize that integrating multi-source features and leveraging an attention mechanism that…
Van Thuy Hoang, Hyeon-Ju Jeon, Eun-Soon You, Yoewon Yoon + 3 more
Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc. Graph representation learning aims to map graph entities to low-dimensional…
Ling Chen, Zhuofeng Li, Yuntong Hu, Zheng Zhang + 3 more
'Shuang Zheng' 'Liang Zhao'] Textual-edge Graphs (TEGs), characterized by rich text annotations on edges, are increasingly significant in network science due to their ability to capture rich contextual information among entities. Existing works have proposed various edge-aware graph neural networks (GNNs) or let…
Authors not listed
Early prediction of drug-induced organ toxicity remains a major bottleneck in drug discovery and clinical pharmacotherapy. Most data-driven toxicity models behave as endpoint predictors: they output a label but provide limited transparency about why a compound is risky or which evidence channel dominated the decision.…
Bastian Pfeifer, Afan Secic, Anna Saranti, Andreas Holzinger
The tremendous success of graphical neural networks (GNNs) has already had a major impact on systems biology research. For example, GNNs are currently used for drug target recognition in protein-drug interaction networks as well as cancer gene discovery and more. Important aspects whose practical relevance is often…
Zachary Humphreys, Xenophon Evangelopoulos, Stavros Gerolymatos, Edward O. Pyzer-Knapp + 1 more
Graph neural networks have recently met huge success in various inference tasks including materials property prediction amongst many others. Nevertheless, having an inherently locally-based representation capacity as they do, global representation of materials' structures can only only be achieved by expanding the…
Shuo Yu, Yingbo Wang, Ruolin Li, Gang Liu + 6 more
Graph Learning Authors: ['Shuo Yu' 'Yingbo Wang' 'Ruolin Li' 'Gang Liu' 'Yanming Shen' 'Shaoxiong Ji' 'B. Li' 'Fengling Han' 'Xiuzhen Zhang' 'Feng Xia'] Graphs are data structures used to represent irregular networks and are prevalent in numerous real-world applications. Previous methods directly model graph structures…
Margarita Bugueño, Gerard de Melo
Given the success of Graph Neural Networks (GNNs) for structure-aware machine learning, many studies have explored their use for text classification, but mostly in specific domains with limited data characteristics. Moreover, some strategies prior to GNNs relied on graph mining and classical machine learning, making it…
Srijani Bagchi, Anasua Sarkar, Ujjwal Maulik
In times like this, it is imperative to be cautious about the effects of drugs or vaccination doses on patients who are already suffering from other serious diseases. It’s not only the virus which can affect the body metabolisms, drugs to encounter the virus may also end up having unwanted negative effects. Therapeutic…
Gowri Nayar, Russ B. Altman
Understanding protein function requires integrating diverse biological evidence while accounting for strong contextual dependence. Recent protein embedding methods increasingly leverage heterogeneous biological networks, yet their evaluation protocols often fail to reflect the specific biological tasks for which the…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
Authors not listed
Predicting molecular dipole moments is essential for quantum chemistry and materials science applications. In this study, we introduce Q-DFTNet: a Chemistry-Informed Neural Network framework designed to systematically benchmark and interpret graph neural networks (GNNs) for molecular dipole prediction. Seven GNN…
Authors not listed
Graph Neural Networks (GNNs) have emerged as a powerful tool in predicting molecular properties based on structural data. While GNNs excel in identifying local patterns within molecules, their ability to capture global properties remains limited due to inherent structural challenges such as oversmoothing and their…