Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks

• URL: http://arxiv.org/abs/2010.06310v2
• Date: Wed, 14 Oct 2020 02:11:09 GMT
• Title: Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks
• Authors: Yue Wang, Zhuo Xu, Lu Bai, Yao Wan, Lixin Cui, Qian Zhao, Edwin R. Hancock, Philip S. Yu
• Abstract summary: 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.
• Score: 61.950353376870154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Joint-event-extraction, which extracts structural information (i.e., entities or triggers of events) from unstructured real-world corpora, has attracted more and more research attention in natural language processing. Most existing works do not fully address the sparse co-occurrence relationships between entities and triggers, which loses this important information and thus deteriorates the extraction performance. To mitigate this issue, we first define the joint-event-extraction as a sequence-to-sequence labeling task with a tag set composed of tags of triggers and entities. Then, to incorporate the missing information in the aforementioned co-occurrence relationships, we propose a Cross-Supervised Mechanism (CSM) to alternately supervise the extraction of either triggers or entities based on the type distribution of each other. Moreover, since the connected entities and triggers naturally form a heterogeneous information network (HIN), we leverage the latent pattern along meta-paths for a given corpus to further improve the performance of our proposed method. To verify the effectiveness of our proposed method, we conduct extensive experiments on four real-world datasets as well as compare our method with state-of-the-art methods. Empirical results and analysis show that our approach outperforms the state-of-the-art methods in both entity and trigger extraction.

Related papers

• EnriCo: Enriched Representation and Globally Constrained Inference for Entity and Relation Extraction [3.579132482505273]
  Joint entity and relation extraction plays a pivotal role in various applications, notably in the construction of knowledge graphs. Existing approaches often fall short of two key aspects: richness of representation and coherence in output structure. In our work, we introduce EnriCo, which mitigates these shortcomings.
  arXiv  Detail & Related papers  (2024-04-18T20:15:48Z)
• CARE: Co-Attention Network for Joint Entity and Relation Extraction [0.0]
  We propose a Co-Attention network for joint entity and relation extraction. Our approach includes adopting a parallel encoding strategy to learn separate representations for each subtask. At the core of our approach is the co-attention module that captures two-way interaction between the two subtasks.
  arXiv  Detail & Related papers  (2023-08-24T03:40:54Z)
• Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation [13.418617500641401]
  We propose Jointprop, a Heterogeneous Graph-based Propagation framework for joint semi-supervised entity and relation extraction. We construct a unified span-based heterogeneous graph from entity and relation candidates and propagate class labels based on confidence scores. We show that our framework outperforms the state-of-the-art semi-supervised approaches on NER and RE tasks.
  arXiv  Detail & Related papers  (2023-05-25T09:07:04Z)
• HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction [60.80849503639896]
  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.
  arXiv  Detail & Related papers  (2022-05-04T17:56:48Z)
• 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 Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Element Intervention for Open Relation Extraction [27.408443348900057]
  OpenRE aims to cluster relation instances referring to the same underlying relation. Current OpenRE models are commonly trained on the datasets generated from distant supervision. In this paper, we revisit the procedure of OpenRE from a causal view.
  arXiv  Detail & Related papers  (2021-06-17T14:37:13Z)
• End-to-End Hierarchical Relation Extraction for Generic Form Understanding [0.6299766708197884]
  We present a novel deep neural network to jointly perform both entity detection and link prediction. Our model extends the Multi-stage Attentional U-Net architecture with the Part-Intensity Fields and Part-Association Fields for link prediction. We demonstrate the effectiveness of the model on the Form Understanding in Noisy Scanned Documents dataset.
  arXiv  Detail & Related papers  (2021-06-02T06:51:35Z)
• Joint Constrained Learning for Event-Event Relation Extraction [94.3499255880101]
  We propose a joint constrained learning framework for modeling event-event relations. Specifically, the framework enforces logical constraints within and across multiple temporal and subevent relations. We show that our joint constrained learning approach effectively compensates for the lack of jointly labeled data.
  arXiv  Detail & Related papers  (2020-10-13T22:45:28Z)
• Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents [52.55119217982361]
  We propose a joint extraction approach to handle noisy instances with a group of cooperative multiagents. To handle noisy instances in a fine-grained manner, each agent in the cooperative group evaluates the instance by calculating a continuous confidence score from its own perspective. A confidence consensus module is designed to gather the wisdom of all agents and re-distribute the noisy training set with confidence-scored labels.
  arXiv  Detail & Related papers  (2020-04-21T12:03:04Z)

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.