Cross-domain Named Entity Recognition via Graph Matching

• URL: http://arxiv.org/abs/2408.00981v2
• Date: Thu, 8 Aug 2024 02:15:53 GMT
• Title: Cross-domain Named Entity Recognition via Graph Matching
• Authors: Junhao Zheng, Haibin Chen, Qianli Ma,
• Abstract summary: Cross-domain NER is a practical yet challenging problem since the data scarcity in the real-world scenario. We model the label relationship as a probability distribution and construct label graphs in both source and target label spaces. By representing label relationships as graphs, we formulate cross-domain NER as a graph matching problem.
• Score: 25.237288970802425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cross-domain NER is a practical yet challenging problem since the data scarcity in the real-world scenario. A common practice is first to learn a NER model in a rich-resource general domain and then adapt the model to specific domains. Due to the mismatch problem between entity types across domains, the wide knowledge in the general domain can not effectively transfer to the target domain NER model. To this end, we model the label relationship as a probability distribution and construct label graphs in both source and target label spaces. To enhance the contextual representation with label structures, we fuse the label graph into the word embedding output by BERT. By representing label relationships as graphs, we formulate cross-domain NER as a graph matching problem. Furthermore, the proposed method has good applicability with pre-training methods and is potentially capable of other cross-domain prediction tasks. Empirical results on four datasets show that our method outperforms a series of transfer learning, multi-task learning, and few-shot learning methods.

Related papers

• SA-GDA: Spectral Augmentation for Graph Domain Adaptation [38.71041292000361]
  Graph neural networks (GNNs) have achieved impressive impressions for graph-related tasks. This paper presents the textitSpectral Augmentation for Graph Domain Adaptation (method) for graph node classification. We develop a dual graph convolutional network to jointly exploits local and global consistency for feature aggregation.
  arXiv  Detail & Related papers  (2024-08-17T13:01:45Z)
• Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains [8.02139126500224]
  This paper proposes an adaptive graph-based approach for multi-label image classification. It is done by integrating an attention-based mechanism and a similarity-preserving strategy. The proposed framework is then extended to multiple domains using an adversarial training scheme.
  arXiv  Detail & Related papers  (2023-01-11T14:42:47Z)
• Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains [3.358838755118655]
  We introduce Domain-versaational Variational Graph Autoencoders (DAVGAE) to solve this cross-domain prerequisite chain learning task efficiently. Our novel model consists of a variational graph autoencoder (VGAE) and a domain discriminator. Results show that our model outperforms recent graph-based computation using only 1/10 graph scale and 1/3 time.
  arXiv  Detail & Related papers  (2021-09-17T19:07:27Z)
• Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation [71.77083272602525]
  UDA attempts to provide efficient knowledge transfer from a labeled source domain to an unlabeled target domain. We propose a contrastive learning approach that adapts category-wise centroids across domains. We extend our method with self-training, where we use a memory-efficient temporal ensemble to generate consistent and reliable pseudo-labels.
  arXiv  Detail & Related papers  (2021-05-05T11:55:53Z)
• Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation [78.28390172958643]
  We identify two key aspects that can help to alleviate multiple domain-shifts in the multi-target domain adaptation (MTDA) We propose Curriculum Graph Co-Teaching (CGCT) that uses a dual classifier head, with one of them being a graph convolutional network (GCN) which aggregates features from similar samples across the domains. When the domain labels are available, we propose Domain-aware Curriculum Learning (DCL), a sequential adaptation strategy that first adapts on the easier target domains, followed by the harder ones.
  arXiv  Detail & Related papers  (2021-04-01T23:41:41Z)
• Inferring Latent Domains for Unsupervised Deep Domain Adaptation [54.963823285456925]
  Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain where labeled data are not available. This paper introduces a novel deep architecture which addresses the problem of UDA by automatically discovering latent domains in visual datasets. We evaluate our approach on publicly available benchmarks, showing that it outperforms state-of-the-art domain adaptation methods.
  arXiv  Detail & Related papers  (2021-03-25T14:33:33Z)
• Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer [140.72439827136085]
  We propose a graph reasoning and transfer learning framework named "Graphonomy" It incorporates human knowledge and label taxonomy into the intermediate graph representation learning beyond local convolutions. It learns the global and structured semantic coherency in multiple domains via semantic-aware graph reasoning and transfer.
  arXiv  Detail & Related papers  (2021-01-26T08:19:03Z)
• Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation [56.694330303488435]
  We propose a Learning to Combine for Multi-Source Domain Adaptation (LtC-MSDA) framework. In the nutshell, a knowledge graph is constructed on the prototypes of various domains to realize the information propagation among semantically adjacent representations. Our approach outperforms existing methods with a remarkable margin.
  arXiv  Detail & Related papers  (2020-07-17T07:52:44Z)
• Named Entity Recognition without Labelled Data: A Weak Supervision Approach [23.05371427663683]
  This paper presents a simple but powerful approach to learn NER models in the absence of labelled data through weak supervision. The approach relies on a broad spectrum of labelling functions to automatically annotate texts from the target domain. A sequence labelling model can finally be trained on the basis of this unified annotation.
  arXiv  Detail & Related papers  (2020-04-30T12:29:55Z)
• Deep Domain-Adversarial Image Generation for Domain Generalisation [115.21519842245752]
  Machine learning models typically suffer from the domain shift problem when trained on a source dataset and evaluated on a target dataset of different distribution. To overcome this problem, domain generalisation (DG) methods aim to leverage data from multiple source domains so that a trained model can generalise to unseen domains. We propose a novel DG approach based on emphDeep Domain-Adversarial Image Generation (DDAIG)
  arXiv  Detail & Related papers  (2020-03-12T23:17:47Z)

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.