Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction

• URL: http://arxiv.org/abs/2102.10249v1
• Date: Sat, 20 Feb 2021 03:47:46 GMT
• Title: Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction
• Authors: Benfeng Xu, Quan Wang, Yajuan Lyu, Yong Zhu, Zhendong Mao
• Abstract summary: We formulate such structure as distinctive dependencies between mention pairs. We then propose SSAN, which incorporates these structural dependencies within the standard self-attention mechanism. Our experiments demonstrate the usefulness of the proposed entity structure and the effectiveness of SSAN.
• Score: 17.653025734439616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entities, as the essential elements in relation extraction tasks, exhibit certain structure. In this work, we formulate such structure as distinctive dependencies between mention pairs. We then propose SSAN, which incorporates these structural dependencies within the standard self-attention mechanism and throughout the overall encoding stage. Specifically, we design two alternative transformation modules inside each self-attention building block to produce attentive biases so as to adaptively regularize its attention flow. Our experiments demonstrate the usefulness of the proposed entity structure and the effectiveness of SSAN. It significantly outperforms competitive baselines, achieving new state-of-the-art results on three popular document-level relation extraction datasets. We further provide ablation and visualization to show how the entity structure guides the model for better relation extraction. Our code is publicly available.

Related papers

• Zero-Shot Open-Schema Entity Structure Discovery [24.209745835571475]
  We introduce ZOES, a novel approach to entity structure extraction based on a principled mechanism of enrichment, refinement, and unification.<n> ZOES consistently enhances LLMs' ability to extract more complete entity structures across three different domains.
  arXiv  Detail & Related papers  (2025-06-04T21:18:39Z)
• 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)
• GraphER: A Structure-aware Text-to-Graph Model for Entity and Relation Extraction [3.579132482505273]
  Information extraction is an important task in Natural Language Processing (NLP) We propose a novel approach to this task by formulating it as graph structure learning (GSL) This formulation allows for better interaction and structure-informed decisions for entity and relation prediction.
  arXiv  Detail & Related papers  (2024-04-18T20:09:37Z)
• 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)
• Global Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Document [37.05334263712291]
  We propose a GlObal Structure knowledge-guided relation Extraction (GOSE) framework. GOSE initiates by generating preliminary relation predictions on entity pairs extracted from a scanned image of the document. Global structure knowledge is captured from the preceding iterative predictions, which are then incorporated into the representations of the entities.
  arXiv  Detail & Related papers  (2023-05-23T09:18:47Z)
• ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select [53.071352033539526]
  We study the problem of extracting N-ary relations from scientific articles. Our proposed method ReSel decomposes this task into a two-stage procedure. Our experiments on three scientific information extraction datasets show that ReSel outperforms state-of-the-art baselines significantly.
  arXiv  Detail & Related papers  (2022-10-26T02:28:02Z)
• Modeling Multi-Granularity Hierarchical Features for Relation Extraction [26.852869800344813]
  We propose a novel method to extract multi-granularity features based solely on the original input sentences. We show that effective structured features can be attained even without external knowledge.
  arXiv  Detail & Related papers  (2022-04-09T09:44:05Z)
• Unified Structure Generation for Universal Information Extraction [58.89057387608414]
  UIE can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources. Experiments show that UIE achieved the state-of-the-art performance on 4 IE tasks, 13 datasets, and on all supervised, low-resource, and few-shot settings.
  arXiv  Detail & Related papers  (2022-03-23T08:49:29Z)
• Neural Production Systems [90.75211413357577]
  Visual environments are structured, consisting of distinct objects or entities. To partition images into entities, deep-learning researchers have proposed structural inductive biases. We take inspiration from cognitive science and resurrect a classic approach, which consists of a set of rule templates. This architecture achieves a flexible, dynamic flow of control and serves to factorize entity-specific and rule-based information.
  arXiv  Detail & Related papers  (2021-03-02T18:53:20Z)
• 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)
• A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis [73.74885246830611]
  We propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA. This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn) Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-04T14:59:32Z)

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.