Multi-Relation Extraction in Entity Pairs using Global Context

• URL: http://arxiv.org/abs/2507.22926v1
• Date: Wed, 23 Jul 2025 13:24:32 GMT
• Title: Multi-Relation Extraction in Entity Pairs using Global Context
• Authors: Nilesh, Atul Gupta, Avinash C Panday,
• Abstract summary: This paper introduces a novel input embedding approach to capture the positions of mentioned entities throughout a document.<n>The performance of the proposed method has been tested on three benchmark relation extraction datasets.<n>Theoretically, it advances global context modeling and multi-sentence reasoning in document-level relation extraction.
• Score: 0.8437187555622164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In document-level relation extraction, entities may appear multiple times in a document, and their relationships can shift from one context to another. Accurate prediction of the relationship between two entities across an entire document requires building a global context spanning all relevant sentences. Previous approaches have focused only on the sentences where entities are mentioned, which fails to capture the complete document context necessary for accurate relation extraction. Therefore, this paper introduces a novel input embedding approach to capture the positions of mentioned entities throughout the document rather than focusing solely on the span where they appear. The proposed input encoding approach leverages global relationships and multi-sentence reasoning by representing entities as standalone segments, independent of their positions within the document. The performance of the proposed method has been tested on three benchmark relation extraction datasets, namely DocRED, Re-DocRED, and REBEL. The experimental results demonstrated that the proposed method accurately predicts relationships between entities in a document-level setting. The proposed research also has theoretical and practical implications. Theoretically, it advances global context modeling and multi-sentence reasoning in document-level relation extraction. Practically, it enhances relationship detection, enabling improved performance in real-world NLP applications requiring comprehensive entity-level insights and interpretability.

Related papers

• Unified Multimodal Interleaved Document Representation for Retrieval [57.65409208879344]
  We propose a method that holistically embeds documents interleaved with multiple modalities.<n>We merge the representations of segmented passages into one single document representation.<n>We show that our approach substantially outperforms relevant baselines.
  arXiv  Detail & Related papers  (2024-10-03T17:49:09Z)
• Anaphor Assisted Document-Level Relation Extraction [42.57958231709678]
  Document-level relation extraction (DocRE) involves identifying relations between entities distributed in multiple sentences within a document. Existing methods focus on building a heterogeneous document graph to model the internal structure of an entity and the external interaction between entities. We propose an Anaphor-Assisted (AA) framework for DocRE tasks.
  arXiv  Detail & Related papers  (2023-10-28T06:11:18Z)
• A Comprehensive Survey of Document-level Relation Extraction (2016-2023) [3.0204640945657326]
  Document-level relation extraction (DocRE) is an active area of research in natural language processing (NLP) This paper aims to provide a comprehensive overview of recent advances in this field, highlighting its different applications in comparison to sentence-level relation extraction.
  arXiv  Detail & Related papers  (2023-09-28T12:43:32Z)
• PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition [63.51569687229681]
  We argue for the need to recognize the textual entailment relation of each proposition in a sentence individually. We propose PropSegmEnt, a corpus of over 45K propositions annotated by expert human raters. Our dataset structure resembles the tasks of (1) segmenting sentences within a document to the set of propositions, and (2) classifying the entailment relation of each proposition with respect to a different yet topically-aligned document.
  arXiv  Detail & Related papers  (2022-12-21T04:03:33Z)
• Relational Proxies: Emergent Relationships as Fine-Grained Discriminators [52.17542855760418]
  We propose a novel approach that leverages information between the global and local part of an object for encoding its label. We design Proxies based on our theoretical findings and evaluate it on seven challenging fine-grained benchmark datasets. We also experimentally validate our theory and obtain consistent results across multiple benchmarks.
  arXiv  Detail & Related papers  (2022-10-05T11:08:04Z)
• Document-Level Relation Extraction with Sentences Importance Estimation and Focusing [52.069206266557266]
  Document-level relation extraction (DocRE) aims to determine the relation between two entities from a document of multiple sentences. We propose a Sentence Estimation and Focusing (SIEF) framework for DocRE, where we design a sentence importance score and a sentence focusing loss. Experimental results on two domains show that our SIEF not only improves overall performance, but also makes DocRE models more robust.
  arXiv  Detail & Related papers  (2022-04-27T03:20:07Z)
• 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)
• Eider: Evidence-enhanced Document-level Relation Extraction [56.71004595444816]
  Document-level relation extraction (DocRE) aims at extracting semantic relations among entity pairs in a document. We propose a three-stage evidence-enhanced DocRE framework consisting of joint relation and evidence extraction, evidence-centered relation extraction (RE), and fusion of extraction results.
  arXiv  Detail & Related papers  (2021-06-16T09:43:16Z)
• Document-level Relation Extraction as Semantic Segmentation [38.614931876015625]
  Document-level relation extraction aims to extract relations among multiple entity pairs from a document. This paper approaches the problem by predicting an entity-level relation matrix to capture local and global information. We propose a Document U-shaped Network for document-level relation extraction.
  arXiv  Detail & Related papers  (2021-06-07T13:44:44Z)
• Global-to-Local Neural Networks for Document-Level Relation Extraction [11.900280120655898]
  Relation extraction (RE) aims to identify the semantic relations between named entities in text. Recent years have witnessed it raised to the document level, which requires complex reasoning with entities and mentions throughout an entire document. We propose a novel model to document-level RE, by encoding the document information in terms of entity global and local representations as well as context relation representations.
  arXiv  Detail & Related papers  (2020-09-22T07:30:19Z)
• Towards Making the Most of Context in Neural Machine Translation [112.9845226123306]
  We argue that previous research did not make a clear use of the global context. We propose a new document-level NMT framework that deliberately models the local context of each sentence.
  arXiv  Detail & Related papers  (2020-02-19T03:30:00Z)

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.