A Relation-Oriented Clustering Method for Open Relation Extraction

• URL: http://arxiv.org/abs/2109.07205v1
• Date: Wed, 15 Sep 2021 10:46:39 GMT
• Title: A Relation-Oriented Clustering Method for Open Relation Extraction
• Authors: Jun Zhao, Tao Gui, Qi Zhang, and Yaqian Zhou
• Abstract summary: We propose a relation-oriented clustering model and use it to identify the novel relations in the unlabeled data. We minimize distance between the instance with same relation by gathering the instances towards their corresponding relation centroids. Experimental results show that our method reduces the error rate by 29.2% and 15.7%, on two datasets respectively.
• Score: 18.20811491136624
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The clustering-based unsupervised relation discovery method has gradually become one of the important methods of open relation extraction (OpenRE). However, high-dimensional vectors can encode complex linguistic information which leads to the problem that the derived clusters cannot explicitly align with the relational semantic classes. In this work, we propose a relation-oriented clustering model and use it to identify the novel relations in the unlabeled data. Specifically, to enable the model to learn to cluster relational data, our method leverages the readily available labeled data of pre-defined relations to learn a relation-oriented representation. We minimize distance between the instance with same relation by gathering the instances towards their corresponding relation centroids to form a cluster structure, so that the learned representation is cluster-friendly. To reduce the clustering bias on predefined classes, we optimize the model by minimizing a joint objective on both labeled and unlabeled data. Experimental results show that our method reduces the error rate by 29.2% and 15.7%, on two datasets respectively, compared with current SOTA methods.

Related papers

• Self Supervised Correlation-based Permutations for Multi-View Clustering [7.972599673048582]
  We propose an end-to-end deep learning-based MVC framework for general data. Our approach involves learning meaningful fused data representations with a novel permutation-based canonical correlation objective. We demonstrate the effectiveness of our model using ten MVC benchmark datasets.
  arXiv  Detail & Related papers  (2024-02-26T08:08:30Z)
• Graph-based Active Learning for Entity Cluster Repair [1.7453520331111723]
  Cluster repair methods aim to determine errors in clusters and modify them so that each cluster consists of records representing the same entity. Current cluster repair methodologies assume duplicate-free data sources, where each record from one source corresponds to a unique record from another. Recent approaches apply clustering methods in combination with link categorization methods so they can be applied to data sources with duplicates. We introduce a novel approach for cluster repair that utilizes graph metrics derived from the underlying similarity graphs.
  arXiv  Detail & Related papers  (2024-01-26T16:42:49Z)
• Actively Supervised Clustering for Open Relation Extraction [42.114747195195655]
  We present a novel setting, named actively supervised clustering for OpenRE. The key to the setting is selecting which instances to label. We propose a new strategy, which is applicable to dynamically discover clusters of unknown relations.
  arXiv  Detail & Related papers  (2023-06-08T06:55:02Z)
• Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework [74.25493157757943]
  We formulate a novel clustering model, which exploits the non-negative feature property and incorporates the multi-view information into a unified joint learning framework. We also explore, for the first time, the multi-model non-negative graph-based approach to clustering data based on deep features.
  arXiv  Detail & Related papers  (2022-11-03T08:18:27Z)
• ACTIVE:Augmentation-Free Graph Contrastive Learning for Partial Multi-View Clustering [52.491074276133325]
  We propose an augmentation-free graph contrastive learning framework to solve the problem of partial multi-view clustering. The proposed approach elevates instance-level contrastive learning and missing data inference to the cluster-level, effectively mitigating the impact of individual missing data on clustering.
  arXiv  Detail & Related papers  (2022-03-01T02:32:25Z)
• Mind Your Clever Neighbours: Unsupervised Person Re-identification via Adaptive Clustering Relationship Modeling [19.532602887109668]
  Unsupervised person re-identification (Re-ID) attracts increasing attention due to its potential to resolve the scalability problem of supervised Re-ID models. Most existing unsupervised methods adopt an iterative clustering mechanism, where the network was trained based on pseudo labels generated by unsupervised clustering. To generate high-quality pseudo-labels and mitigate the impact of clustering errors, we propose a novel clustering relationship modeling framework for unsupervised person Re-ID.
  arXiv  Detail & Related papers  (2021-12-03T10:55:07Z)
• Graph Contrastive Clustering [131.67881457114316]
  We propose a novel graph contrastive learning framework, which is then applied to the clustering task and we come up with the Graph Constrastive Clustering(GCC) method. Specifically, on the one hand, the graph Laplacian based contrastive loss is proposed to learn more discriminative and clustering-friendly features. On the other hand, a novel graph-based contrastive learning strategy is proposed to learn more compact clustering assignments.
  arXiv  Detail & Related papers  (2021-04-03T15:32:49Z)
• Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles [66.15398165275926]
  We propose a method that can automatically detect and ignore dataset-specific patterns, which we call dataset biases. Our method trains a lower capacity model in an ensemble with a higher capacity model. We show improvement in all settings, including a 10 point gain on the visual question answering dataset.
  arXiv  Detail & Related papers  (2020-11-07T22:20:03Z)
• Clustering-based Unsupervised Generative Relation Extraction [3.342376225738321]
  We propose a Clustering-based Unsupervised generative Relation Extraction framework (CURE) We use an "Encoder-Decoder" architecture to perform self-supervised learning so the encoder can extract relation information. Our model performs better than state-of-the-art models on both New York Times (NYT) and United Nations Parallel Corpus (UNPC) standard datasets.
  arXiv  Detail & Related papers  (2020-09-26T20:36:40Z)
• Relation-Guided Representation Learning [53.60351496449232]
  We propose a new representation learning method that explicitly models and leverages sample relations. Our framework well preserves the relations between samples. By seeking to embed samples into subspace, we show that our method can address the large-scale and out-of-sample problem.
  arXiv  Detail & Related papers  (2020-07-11T10:57:45Z)
• Decorrelated Clustering with Data Selection Bias [55.91842043124102]
  We propose a novel Decorrelation regularized K-Means algorithm (DCKM) for clustering with data selection bias. Our DCKM algorithm achieves significant performance gains, indicating the necessity of removing unexpected feature correlations induced by selection bias.
  arXiv  Detail & Related papers  (2020-06-29T08:55:50Z)

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.