HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction

• URL: http://arxiv.org/abs/2205.02225v2
• Date: Thu, 5 May 2022 19:08:32 GMT
• Title: HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction
• Authors: Shuliang Liu, Xuming Hu, Chenwei Zhang, Shu`ang Li, Lijie Wen, Philip S. Yu
• Abstract summary: Unsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution. We propose a novel contrastive learning framework named HiURE, which has the capability to derive hierarchical signals from relational feature space using cross hierarchy attention. Experimental results on two public datasets demonstrate the advanced effectiveness and robustness of HiURE on unsupervised relation extraction when compared with state-of-the-art models.
• Score: 60.80849503639896
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Unsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution. Existing works either utilize self-supervised schemes to refine relational feature signals by iteratively leveraging adaptive clustering and classification that provoke gradual drift problems, or adopt instance-wise contrastive learning which unreasonably pushes apart those sentence pairs that are semantically similar. To overcome these defects, we propose a novel contrastive learning framework named HiURE, which has the capability to derive hierarchical signals from relational feature space using cross hierarchy attention and effectively optimize relation representation of sentences under exemplar-wise contrastive learning. Experimental results on two public datasets demonstrate the advanced effectiveness and robustness of HiURE on unsupervised relation extraction when compared with state-of-the-art models.

Related papers

• Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extraction [4.7220779071424985]
  Few-shot Relation Extraction (FSRE) aims to extract facts from a sparse set of labeled corpora. Recent studies have shown promising results in FSRE by employing Pre-trained Language Models. We introduce a novel synergistic anchored contrastive pre-training framework.
  arXiv  Detail & Related papers  (2023-12-19T10:16:24Z)
• Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs [15.87963432758696]
  Un-sentence relation extraction (URE) aims to extract relations between named entities from raw text. We propose AugURE with both within-sentence pairs augmentation and augmentation through cross-sentence pairs extraction. Experiments on NYT-FB and TACRED datasets demonstrate that the proposed relation representation learning and a simple K-Means clustering achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-12-01T12:59:32Z)
• Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition [4.7938839332508945]
  We propose a Prompt-based Logical Semantics Enhancement (PLSE) method for Implicit Discourse Relation Recognition (IDRR) Our method seamlessly injects knowledge relevant to discourse relation into pre-trained language models through prompt-based connective prediction. Experimental results on PDTB 2.0 and CoNLL16 datasets demonstrate that our method achieves outstanding and consistent performance against the current state-of-the-art models.
  arXiv  Detail & Related papers  (2023-11-01T08:38:08Z)
• 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)
• Learning Complete Topology-Aware Correlations Between Relations for Inductive Link Prediction [121.65152276851619]
  We show that semantic correlations between relations are inherently edge-level and entity-independent. We propose a novel subgraph-based method, namely TACO, to model Topology-Aware COrrelations between relations. To further exploit the potential of RCN, we propose Complete Common Neighbor induced subgraph.
  arXiv  Detail & Related papers  (2023-09-20T08:11:58Z)
• 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)
• Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training [49.9995628166064]
  We propose CTEG, a model equipped with two mechanisms to learn to decouple easily-confused relations. On the one hand, an EGA mechanism is introduced to guide the attention to filter out information causing confusion. On the other hand, a Confusion-Aware Training (CAT) method is proposed to explicitly learn to distinguish relations.
  arXiv  Detail & Related papers  (2020-10-21T11:07:53Z)
• Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks [61.950353376870154]
  Joint-event-extraction is a sequence-to-sequence labeling task with a tag set composed of tags of triggers and entities. We propose a Cross-Supervised Mechanism (CSM) to alternately supervise the extraction of triggers or entities. Our approach outperforms the state-of-the-art methods in both entity and trigger extraction.
  arXiv  Detail & Related papers  (2020-10-13T11:51:17Z)
• SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction [60.08464995629325]
  Open-domain relation extraction is the task of extracting open-domain relation facts from natural language sentences. We proposed a self-supervised framework named SelfORE, which exploits weak, self-supervised signals. Experimental results on three datasets show the effectiveness and robustness of SelfORE.
  arXiv  Detail & Related papers  (2020-04-06T07:23:17Z)

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.