Multi-Attribute Relation Extraction (MARE) -- Simplifying the Application of Relation Extraction

• URL: http://arxiv.org/abs/2111.09035v1
• Date: Wed, 17 Nov 2021 11:06:39 GMT
• Title: Multi-Attribute Relation Extraction (MARE) -- Simplifying the Application of Relation Extraction
• Authors: Lars Kl\"oser, Philipp Kohl, Bodo Kraft, Albert Z\"undorf
• Abstract summary: Natural language understanding's relation extraction makes innovative and encouraging novel business concepts possible. Current approaches allow the extraction of relations with a fixed number of entities as attributes. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches.
• Score: 3.1255943277671894
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Natural language understanding's relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. Avoiding elaborated annotation constraints simplifies the application of relation extraction approaches. The evaluation compares our models to current state-of-the-art event extraction and binary relation extraction methods. Our approaches show improvement compared to these on the extraction of general multi-attribute relations.

Related papers

• Learning to Extract Structured Entities Using Language Models [52.281701191329]
  Recent advances in machine learning have significantly impacted the field of information extraction. We reformulate the task to be entity-centric, enabling the use of diverse metrics. We contribute to the field by introducing Structured Entity Extraction and proposing the Approximate Entity Set OverlaP metric.
  arXiv  Detail & Related papers  (2024-02-06T22:15:09Z)
• Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction [19.019881161010474]
  Relation extraction is essentially a text classification problem, which can be tackled by fine-tuning a pre-trained language model (LM) Existing approaches therefore solve the problem in an indirect way: they fine-tune an LM to learn embeddings of the head and tail entities, and then predict the relationship from these entity embeddings. Our hypothesis in this paper is that relation extraction models can be improved by capturing relationships in a more direct way.
  arXiv  Detail & Related papers  (2023-12-18T09:58:19Z)
• Zero-Shot Dialogue Relation Extraction by Relating Explainable Triggers and Relation Names [28.441725610692714]
  This paper proposes a method for leveraging the ability to capture triggers and relate them to previously unseen relation names. Our experiments on a benchmark DialogRE dataset demonstrate that the proposed model achieves significant improvements for both seen and unseen relations.
  arXiv  Detail & Related papers  (2023-06-09T07:10:01Z)
• 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)
• D-REX: Dialogue Relation Extraction with Explanations [65.3862263565638]
  This work focuses on extracting explanations that indicate that a relation exists while using only partially labeled data. We propose our model-agnostic framework, D-REX, a policy-guided semi-supervised algorithm that explains and ranks relations. We find that about 90% of the time, human annotators prefer D-REX's explanations over a strong BERT-based joint relation extraction and explanation model.
  arXiv  Detail & Related papers  (2021-09-10T22:30:48Z)
• Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction [84.64435075778988]
  We propose a general approach to learn relation prototypes from unlabeled texts. We learn relation prototypes as an implicit factor between entities. We conduct experiments on two publicly available datasets: New York Times and Google Distant Supervision.
  arXiv  Detail & Related papers  (2020-11-27T06:21:12Z)
• Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction [40.00702385889112]
  We propose a novel multi-prototype embedding network model to jointly extract the composition of relational triples. We design a hybrid learning mechanism that bridges text and knowledge concerning both entities and relations. Experimental results demonstrate that the proposed method can improve the performance of the few-shot triple extraction.
  arXiv  Detail & Related papers  (2020-10-30T04:18:39Z)
• Relation of the Relations: A New Paradigm of the Relation Extraction Problem [52.21210549224131]
  We propose a new paradigm of Relation Extraction (RE) that considers as a whole the predictions of all relations in the same context. We develop a data-driven approach that does not require hand-crafted rules but learns by itself the relation of relations (RoR) using Graph Neural Networks and a relation matrix transformer. Experiments show that our model outperforms the state-of-the-art approaches by +1.12% on the ACE05 dataset and +2.55% on SemEval 2018 Task 7.2.
  arXiv  Detail & Related papers  (2020-06-05T22:25:27Z)
• 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)
• A logic-based relational learning approach to relation extraction: The OntoILPER system [0.9176056742068812]
  We present OntoILPER, a logic-based relational learning approach to Relation Extraction. OntoILPER takes profit of a rich relational representation of examples, which can alleviate the drawbacks. The proposed relational approach seems to be more suitable for Relation Extraction than statistical ones.
  arXiv  Detail & Related papers  (2020-01-13T12:47:49Z)

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.