Attending To Syntactic Information In Biomedical Event Extraction Via Graph Neural Networks

• URL: http://arxiv.org/abs/2501.01158v2
• Date: Tue, 21 Jan 2025 09:12:40 GMT
• Title: Attending To Syntactic Information In Biomedical Event Extraction Via Graph Neural Networks
• Authors: Farshad Noravesh, Reza Haffari, Ong Huey Fang, Layki Soon, Sailaja Rajalana, Arghya Pal,
• Abstract summary: This paper uses the full adjacency matrix of the dependency graph to embed individual tokens.<n>An ablation study is also done to show the effect of the dependency graph on the overall performance.<n>The proposed model slightly outperforms state-of-the-art models on BEE over different datasets.
• Score: 5.758308856138859
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many models are proposed in the literature on biomedical event extraction(BEE). Some of them use the shortest dependency path(SDP) information to represent the argument classification task. There is an issue with this representation since even missing one word from the dependency parsing graph may totally change the final prediction. To this end, the full adjacency matrix of the dependency graph is used to embed individual tokens using a graph convolutional network(GCN). An ablation study is also done to show the effect of the dependency graph on the overall performance. The results show a significant improvement when dependency graph information is used. The proposed model slightly outperforms state-of-the-art models on BEE over different datasets.

Related papers

• Beyond Message Passing: Neural Graph Pattern Machine [50.78679002846741]
  We introduce the Neural Graph Pattern Machine (GPM), a novel framework that bypasses message passing by learning directly from graph substructures.<n>GPM efficiently extracts, encodes, and prioritizes task-relevant graph patterns, offering greater expressivity and improved ability to capture long-range dependencies.
  arXiv  Detail & Related papers  (2025-01-30T20:37:47Z)
• Graph Size-imbalanced Learning with Energy-guided Structural Smoothing [13.636616140250908]
  Real-world graphs usually suffer from the size-imbalanced problem in the multi-graph classification. Recent studies find that off-the-shelf Graph Neural Networks (GNNs) would compromise model performance under the long-tailed settings. We propose a novel energy-based size-imbalanced learning framework named textbfSIMBA, which smooths the features between head and tail graphs.
  arXiv  Detail & Related papers  (2024-12-23T14:06:49Z)
• Graph-Dictionary Signal Model for Sparse Representations of Multivariate Data [49.77103348208835]
  We define a novel Graph-Dictionary signal model, where a finite set of graphs characterizes relationships in data distribution through a weighted sum of their Laplacians. We propose a framework to infer the graph dictionary representation from observed data, along with a bilinear generalization of the primal-dual splitting algorithm to solve the learning problem. We exploit graph-dictionary representations in a motor imagery decoding task on brain activity data, where we classify imagined motion better than standard methods.
  arXiv  Detail & Related papers  (2024-11-08T17:40:43Z)
• A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation [59.14165404728197]
  We provide an up-to-date and forward-looking review of deep graph learning under distribution shifts. Specifically, we cover three primary scenarios: graph OOD generalization, training-time graph OOD adaptation, and test-time graph OOD adaptation. To provide a better understanding of the literature, we introduce a systematic taxonomy that classifies existing methods into model-centric and data-centric approaches.
  arXiv  Detail & Related papers  (2024-10-25T02:39:56Z)
• Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization [9.116601683256317]
  In this work, we propose a novel framework, called Invariant Graph Learning based on Information bottleneck theory (InfoIGL) Specifically, InfoIGL introduces a redundancy filter to compress task-irrelevant information related to environmental factors. Experiments on both synthetic and real-world datasets demonstrate that our method achieves state-of-the-art performance under OOD generalization.
  arXiv  Detail & Related papers  (2024-08-03T07:38:04Z)
• Graph Out-of-Distribution Generalization with Controllable Data Augmentation [51.17476258673232]
  Graph Neural Network (GNN) has demonstrated extraordinary performance in classifying graph properties. Due to the selection bias of training and testing data, distribution deviation is widespread. We propose OOD calibration to measure the distribution deviation of virtual samples.
  arXiv  Detail & Related papers  (2023-08-16T13:10:27Z)
• ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning [34.23920789327245]
  We propose ENGAGE, where explanation guides the contrastive augmentation process to preserve the key parts in graphs. We also design two data augmentation schemes on graphs for perturbing structural and feature information, respectively.
  arXiv  Detail & Related papers  (2023-07-03T14:33:14Z)
• Probing Graph Representations [77.7361299039905]
  We use a probing framework to quantify the amount of meaningful information captured in graph representations. Our findings on molecular datasets show the potential of probing for understanding the inductive biases of graph-based models. We advocate for probing as a useful diagnostic tool for evaluating graph-based models.
  arXiv  Detail & Related papers  (2023-03-07T14:58:18Z)
• Robust Causal Graph Representation Learning against Confounding Effects [21.380907101361643]
  We propose Robust Causal Graph Representation Learning (RCGRL) to learn robust graph representations against confounding effects. RCGRL introduces an active approach to generate instrumental variables under unconditional moment restrictions, which empowers the graph representation learning model to eliminate confounders.
  arXiv  Detail & Related papers  (2022-08-18T01:31:25Z)
• Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications [52.65389473899139]
  Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Recent works have shown that considering relationships between input data samples have a positive regularizing effect for the downstream task. We propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications.
  arXiv  Detail & Related papers  (2022-04-01T10:01:37Z)
• Model-Agnostic Augmentation for Accurate Graph Classification [19.824105919844495]
  Graph augmentation is an essential strategy to improve the performance of graph-based tasks. In this work, we introduce five desired properties for effective augmentation. Our experiments on social networks and molecular graphs show that NodeSam and SubMix outperform existing approaches in graph classification.
  arXiv  Detail & Related papers  (2022-02-21T10:37:53Z)
• Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning [40.70562886682939]
  We propose a new principle for unsupervised graph representation learning: Graph-wise Common latent Factor EXtraction (GCFX) GCFX explicitly extract common latent factors from an input graph and achieve improved results on downstream tasks to the current state-of-the-art. Through extensive experiments and analysis, we demonstrate that GCFX is beneficial for graph-level tasks to alleviate distractions caused by local variations of individual nodes or local neighbourhoods.
  arXiv  Detail & Related papers  (2021-12-16T12:22:49Z)
• OOD-GNN: Out-of-Distribution Generalized Graph Neural Network [73.67049248445277]
  Graph neural networks (GNNs) have achieved impressive performance when testing and training graph data come from identical distribution. Existing GNNs lack out-of-distribution generalization abilities so that their performance substantially degrades when there exist distribution shifts between testing and training graph data. We propose an out-of-distribution generalized graph neural network (OOD-GNN) for achieving satisfactory performance on unseen testing graphs that have different distributions with training graphs.
  arXiv  Detail & Related papers  (2021-12-07T16:29:10Z)
• Weakly-supervised Graph Meta-learning for Few-shot Node Classification [53.36828125138149]
  We propose a new graph meta-learning framework -- Graph Hallucination Networks (Meta-GHN) Based on a new robustness-enhanced episodic training, Meta-GHN is meta-learned to hallucinate clean node representations from weakly-labeled data. Extensive experiments demonstrate the superiority of Meta-GHN over existing graph meta-learning studies.
  arXiv  Detail & Related papers  (2021-06-12T22:22:10Z)
• Model-Agnostic Graph Regularization for Few-Shot Learning [60.64531995451357]
  We present a comprehensive study on graph embedded few-shot learning. We introduce a graph regularization approach that allows a deeper understanding of the impact of incorporating graph information between labels. Our approach improves the performance of strong base learners by up to 2% on Mini-ImageNet and 6.7% on ImageNet-FS.
  arXiv  Detail & Related papers  (2021-02-14T05:28:13Z)
• Multilevel Graph Matching Networks for Deep Graph Similarity Learning [79.3213351477689]
  We propose a multi-level graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects. To compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks. Comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baseline models on both the graph-graph classification and graph-graph regression tasks.
  arXiv  Detail & Related papers  (2020-07-08T19:48:19Z)
• Out-of-Sample Representation Learning for Multi-Relational Graphs [8.956321788625894]
  We study the out-of-sample representation learning problem for non-attributed knowledge graphs. We create benchmark datasets for this task, develop several models and baselines, and provide empirical analyses and comparisons of the proposed models and baselines.
  arXiv  Detail & Related papers  (2020-04-28T00:53:01Z)
• Unsupervised Graph Embedding via Adaptive Graph Learning [85.28555417981063]
  Graph autoencoders (GAEs) are powerful tools in representation learning for graph embedding. In this paper, two novel unsupervised graph embedding methods, unsupervised graph embedding via adaptive graph learning (BAGE) and unsupervised graph embedding via variational adaptive graph learning (VBAGE) are proposed. Experimental studies on several datasets validate our design and demonstrate that our methods outperform baselines by a wide margin in node clustering, node classification, and graph visualization tasks.
  arXiv  Detail & Related papers  (2020-03-10T02:33:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.