A Two-Phase Paradigm for Joint Entity-Relation Extraction

• URL: http://arxiv.org/abs/2208.08659v1
• Date: Thu, 18 Aug 2022 06:40:25 GMT
• Title: A Two-Phase Paradigm for Joint Entity-Relation Extraction
• Authors: Bin Ji, Hao Xu, Jie Yu, Shasha Li, Jun Ma, Yuke Ji, Huijun Liu
• Abstract summary: We propose a two-phase paradigm for the span-based joint entity and relation extraction. The two-phase paradigm involves classifying the entities and relations in the first phase, and predicting the types of these entities and relations in the second phase. Experimental results on several datasets demonstrate that the spanbased joint extraction model augmented with the two-phase paradigm and the global features consistently outperforms previous state-of-the-art span-based models for the joint extraction task.
• Score: 11.92606118894611
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task. However, these models sample a large number of negative entities and negative relations during the model training, which are essential but result in grossly imbalanced data distributions and in turn cause suboptimal model performance. In order to address the above issues, we propose a two-phase paradigm for the span-based joint entity and relation extraction, which involves classifying the entities and relations in the first phase, and predicting the types of these entities and relations in the second phase. The two-phase paradigm enables our model to significantly reduce the data distribution gap, including the gap between negative entities and other entities, as well as the gap between negative relations and other relations. In addition, we make the first attempt at combining entity type and entity distance as global features, which has proven effective, especially for the relation extraction. Experimental results on several datasets demonstrate that the spanbased joint extraction model augmented with the two-phase paradigm and the global features consistently outperforms previous state-of-the-art span-based models for the joint extraction task, establishing a new standard benchmark. Qualitative and quantitative analyses further validate the effectiveness the proposed paradigm and the global features.

Related papers

• MITA: Bridging the Gap between Model and Data for Test-time Adaptation [68.62509948690698]
  Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. We propose Meet-In-The-Middle based MITA, which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions.
  arXiv  Detail & Related papers  (2024-10-12T07:02:33Z)
• Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models [83.02797560769285]
  Data-Free Meta-Learning (DFML) aims to derive knowledge from a collection of pre-trained models without accessing their original data. Current methods often overlook the heterogeneity among pre-trained models, which leads to performance degradation due to task conflicts. We propose Task Groupings Regularization, a novel approach that benefits from model heterogeneity by grouping and aligning conflicting tasks.
  arXiv  Detail & Related papers  (2024-05-26T13:11:55Z)
• Robust Training of Federated Models with Extremely Label Deficiency [84.00832527512148]
  Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency. We propose a novel twin-model paradigm, called Twin-sight, designed to enhance mutual guidance by providing insights from different perspectives of labeled and unlabeled data. Our comprehensive experiments on four benchmark datasets provide substantial evidence that Twin-sight can significantly outperform state-of-the-art methods across various experimental settings.
  arXiv  Detail & Related papers  (2024-02-22T10:19:34Z)
• Siamese Representation Learning for Unsupervised Relation Extraction [5.776369192706107]
  Unsupervised relation extraction (URE) aims at discovering underlying relations between named entity pairs from open-domain plain text. Existing URE models utilizing contrastive learning, which attract positive samples and repulse negative samples to promote better separation, have got decent effect. We propose Siamese Representation Learning for Unsupervised Relation Extraction -- a novel framework to simply leverage positive pairs to representation learning.
  arXiv  Detail & Related papers  (2023-10-01T02:57:43Z)
• Dealing with negative samples with multi-task learning on span-based joint entity-relation extraction [0.7252027234425334]
  Recent span-based joint extraction models have demonstrated significant advantages in both entity recognition and relation extraction. This paper introduces a span-based multitask entity-relation joint extraction model.
  arXiv  Detail & Related papers  (2023-09-18T12:28:46Z)
• SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction [51.27558374091491]
  We propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for relation extraction. Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately.
  arXiv  Detail & Related papers  (2021-09-24T17:37:35Z)
• An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning [2.111790330664657]
  We present a joint model for entity-level relation extraction from documents. We achieve state-of-the-art relation extraction results on the DocRED dataset. Our experimental results suggest that a joint approach is on par with task-specific learning, though more efficient due to shared parameters and training steps.
  arXiv  Detail & Related papers  (2021-02-11T12:49:39Z)
• Joint Extraction of Entity and Relation with Information Redundancy Elimination [0.0]
  We propose a joint extraction model to solve the problem of redundant information and overlapping relations of the entity and relation extraction model. This model can directly extract multiple pairs of related entities without generating unrelated information. We also propose a recurrent neural network named-LSTM that enhances the ability of recurrent units to model sentences.
  arXiv  Detail & Related papers  (2020-11-27T05:47:26Z)
• Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling [54.94763543386523]
  Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the ( aggregate) posterior to encourage statistical independence of the latent factors. We present a novel multi-stage modeling approach where the disentangled factors are first learned using a penalty-based disentangled representation learning method. Then, the low-quality reconstruction is improved with another deep generative model that is trained to model the missing correlated latent variables.
  arXiv  Detail & Related papers  (2020-10-25T18:51:15Z)
• A Frustratingly Easy Approach for Entity and Relation Extraction [25.797992240847833]
  We present a simple pipelined approach for entity and relation extraction. We establish the new state-of-the-art on standard benchmarks (ACE04, ACE05 and SciERC) Our approach essentially builds on two independent encoders and merely uses the entity model to construct the input for the relation model.
  arXiv  Detail & Related papers  (2020-10-24T07:14:01Z)
• Accounting for Unobserved Confounding in Domain Generalization [107.0464488046289]
  This paper investigates the problem of learning robust, generalizable prediction models from a combination of datasets. Part of the challenge of learning robust models lies in the influence of unobserved confounders. We demonstrate the empirical performance of our approach on healthcare data from different modalities.
  arXiv  Detail & Related papers  (2020-07-21T08:18:06Z)

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.