# Relation Classification with Entity Type Restriction arXiv: https://arxiv.org/abs/2105.08393 ยท HTML: https://mlsanity.com/paper/2105.08393/ ## Key facts - Cited by 31 papers in the map (data through 14 Oct 2026). - Builds on 12 of its 19 references that are in the map. - First posted on arXiv 18 May 2021. ## Builds on (most cited first, 12 of 12) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015) - [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019) - [KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation](https://mlsanity.com/paper/1911.06136/) (2019) - [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020) - [K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters](https://mlsanity.com/paper/2002.01808/) (2020) - [Knowledge Enhanced Contextual Word Representations](https://mlsanity.com/paper/1909.04164/) (2019) - [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019) - [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://mlsanity.com/paper/1809.10185/) (2018) - [Attention Guided Graph Convolutional Networks for Relation Extraction](https://mlsanity.com/paper/1906.07510/) (2019) - [Improved Natural Language Generation via Loss Truncation](https://mlsanity.com/paper/2004.14589/) (2020) - [Relation Classification as Two-way Span-Prediction](https://mlsanity.com/paper/2010.04829/) (2020) ## Built on by (most cited first, 30 of 31) - [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023) - [GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records](https://mlsanity.com/paper/2203.03540/) (2022) - [When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities](https://mlsanity.com/paper/2307.16376/) (2023) - [Summarization as Indirect Supervision for Relation Extraction](https://mlsanity.com/paper/2205.09837/) (2022) - [Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis](https://mlsanity.com/paper/2205.03784/) (2022) - [How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?](https://mlsanity.com/paper/2305.01555/) (2023) - [Unified Semantic Typing with Meaningful Label Inference](https://mlsanity.com/paper/2205.01826/) (2022) - [TAGPRIME: A Unified Framework for Relational Structure Extraction](https://mlsanity.com/paper/2205.12585/) (2022) - [Generative Prompt Tuning for Relation Classification](https://mlsanity.com/paper/2210.12435/) (2022) - [Reviewing Labels: Label Graph Network with Top-k Prediction Set for Relation Extraction](https://mlsanity.com/paper/2212.14270/) (2022) - [Improving Sentence-Level Relation Extraction through Curriculum Learning](https://mlsanity.com/paper/2107.09332/) (2021) - [Why only Micro-F1? Class Weighting of Measures for Relation Classification](https://mlsanity.com/paper/2205.09460/) (2022) - [Multilingual Relation Classification via Efficient and Effective Prompting](https://mlsanity.com/paper/2210.13838/) (2022) - [FCDS: Fusing Constituency and Dependency Syntax into Document-Level Relation Extraction](https://mlsanity.com/paper/2403.01886/) (2024) - [GAMedX: Generative AI-based Medical Entity Data Extractor Using Large Language Models](https://mlsanity.com/paper/2405.20585/) (2024) - [Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling](https://mlsanity.com/paper/2211.05343/) (2022) - [REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2206.05123/) (2022) - [On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction](https://mlsanity.com/paper/2402.18061/) (2024) - [Do Models Learn the Directionality of Relations? A New Evaluation: Relation Direction Recognition](https://mlsanity.com/paper/2105.09045/) (2021) - [GRAPHCACHE: Message Passing as Caching for Sentence-Level Relation Extraction](https://mlsanity.com/paper/2205.03786/) (2022) - [Continual Contrastive Finetuning Improves Low-Resource Relation Extraction](https://mlsanity.com/paper/2212.10823/) (2022) - [CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation](https://mlsanity.com/paper/2310.12024/) (2023) - [LabelPrompt: Effective Prompt-based Learning for Relation Classification](https://mlsanity.com/paper/2302.08068/) (2023) - [Sentence-Level Relation Extraction via Contrastive Learning with Descriptive Relation Prompts](https://mlsanity.com/paper/2304.04935/) (2023) - [About Evaluation of F1 Score for RECENT Relation Extraction System](https://mlsanity.com/paper/2305.09410/) (2023) - [COMM:Concentrated Margin Maximization for Robust Document-Level Relation Extraction](https://mlsanity.com/paper/2503.13885/) (2025) - [Learning with Silver Standard Data for Zero-shot Relation Extraction](https://mlsanity.com/paper/2211.13883/) (2022) - [A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods](https://mlsanity.com/paper/2310.19055/) (2023) - [A Two Dimensional Feature Engineering Method for Relation Extraction](https://mlsanity.com/paper/2404.04959/) (2024) - [Empowering Few-Shot Relation Extraction with The Integration of Traditional RE Methods and Large Language Models](https://mlsanity.com/paper/2407.08967/) (2024) ## Abstract (arXiv) Relation classification aims to predict a relation between two entities in a sentence. The existing methods regard all relations as the candidate relations for the two entities in a sentence. These methods neglect the restrictions on candidate relations by entity types, which leads to some inappropriate relations being candidate relations. In this paper, we propose a novel paradigm, RElation Classification with ENtity Type restriction (RECENT), which exploits entity types to restrict candidate relations. Specially, the mutual restrictions of relations and entity types are formalized and introduced into relation classification. Besides, the proposed paradigm, RECENT, is model-agnostic. Based on two representative models GCN and SpanBERT respectively, RECENT_GCN and RECENT_SpanBERT are trained in RECENT. Experimental results on a standard dataset indicate that RECENT improves the performance of GCN and SpanBERT by 6.9 and 4.4 F1 points, respectively. Especially, RECENT_SpanBERT achieves a new state-of-the-art on TACRED.