iMETRE: Incorporating Markers of Entity Types for Relation Extraction

• URL: http://arxiv.org/abs/2307.00132v1
• Date: Fri, 30 Jun 2023 20:54:41 GMT
• Title: iMETRE: Incorporating Markers of Entity Types for Relation Extraction
• Authors: N Harsha Vardhan, Manav Chaudhary
• Abstract summary: Sentence-level relation extraction aims to identify the relationship between 2 entities given a contextual sentence. In this paper, we approach the task of relationship extraction in the financial dataset REFinD.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sentence-level relation extraction (RE) aims to identify the relationship between 2 entities given a contextual sentence. While there have been many attempts to solve this problem, the current solutions have a lot of room to improve. In this paper, we approach the task of relationship extraction in the financial dataset REFinD. Our approach incorporates typed entity markers representations and various models finetuned on the dataset, which has allowed us to achieve an F1 score of 69.65% on the validation set. Through this paper, we discuss various approaches and possible limitations.

Related papers

• Few-Shot Joint Multimodal Entity-Relation Extraction via Knowledge-Enhanced Cross-modal Prompt Model [16.03304915788997]
  Joint Multimodal Entity-Relation Extraction (JMERE) is a challenging task that aims to extract entities and their relations from text-image pairs in social media posts. Existing methods for JMERE require large amounts of labeled data. We introduce the textbfKnowledge-textbfEnhanced textbfCross-modal textbfPrompt textbfModel.
  arXiv  Detail & Related papers  (2024-10-18T07:14:54Z)
• Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency [87.16283281290053]
  Previous entity disambiguation (ED) methods adopt a discriminative paradigm, where prediction is made based on matching scores between mention context and candidate entities. We propose CoherentED, an ED system equipped with novel designs aimed at enhancing the coherence of entity predictions. We achieve new state-of-the-art results on popular ED benchmarks, with an average improvement of 1.3 F1 points.
  arXiv  Detail & Related papers  (2023-11-06T16:40:13Z)
• Multi-Grained Multimodal Interaction Network for Entity Linking [65.30260033700338]
  Multimodal entity linking task aims at resolving ambiguous mentions to a multimodal knowledge graph. We propose a novel Multi-GraIned Multimodal InteraCtion Network $textbf(MIMIC)$ framework for solving the MEL task.
  arXiv  Detail & Related papers  (2023-07-19T02:11:19Z)
• Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction [13.454953507205278]
  Multi-Modal Relation Extraction aims at identifying the relation between two entities in texts that contain visual clues. We propose a novel MMRE framework to better capture the deeper correlations of text, entity pair, and image/objects. Our approach achieves excellent performance compared to strong competitors, even in the few-shot situation.
  arXiv  Detail & Related papers  (2023-06-19T15:31:34Z)
• REFinD: Relation Extraction Financial Dataset [7.207699035400335]
  We propose REFinD, the first large-scale annotated dataset of relations, with $sim$29K instances and 22 relations amongst 8 types of entity pairs, generated entirely over financial documents. We observed that various state-of-the-art deep learning models struggle with numeric inference, relational and directional ambiguity.
  arXiv  Detail & Related papers  (2023-05-22T22:40:11Z)
• Enriching Relation Extraction with OpenIE [70.52564277675056]
  Relation extraction (RE) is a sub-discipline of information extraction (IE) In this work, we explore how recent approaches for open information extraction (OpenIE) may help to improve the task of RE. Our experiments over two annotated corpora, KnowledgeNet and FewRel, demonstrate the improved accuracy of our enriched models.
  arXiv  Detail & Related papers  (2022-12-19T11:26:23Z)
• 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)
• 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)
• Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling [34.93480801598084]
  One document commonly contains multiple entity pairs, and one entity pair occurs multiple times in the document associated with multiple possible relations. We propose two novel techniques, adaptive thresholding and localized context pooling, to solve the multi-label and multi-entity problems.
  arXiv  Detail & Related papers  (2020-10-21T20:41:23Z)
• 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)
• Reasoning with Latent Structure Refinement for Document-Level Relation Extraction [20.308845516900426]
  We propose a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph. Specifically, our model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED)
  arXiv  Detail & Related papers  (2020-05-13T13:36:09Z)

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.