Papers with relation extraction task
A Bag-of-concepts Model Improves Relation Extraction in a Narrow Knowledge Domain with Limited Data (N19-3)
Copied to clipboard
| Challenge: | Existing methods for relation extraction on small data sets are time-consuming and expensive. |
| Approach: | They propose an automatic relation extraction task with limited annotated data and a narrow knowledge domain. |
| Outcome: | The proposed method outperforms methods of higher complexity on a small clinical corpus. |
BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes (D19-57)
Copied to clipboard
| Challenge: | In 2011, the Bacteria Biotope Task was conducted for the first time as a part of the BioNLP Shared Task targeting the extraction of useful information regarding bacteria and their habitats. |
| Approach: | They propose two systems for the normalization of entities and the identification of relations between entities given a biomedical text. |
| Outcome: | The proposed method performs as good as deep learning based methods which require labeled data. |
Improving Relation Extraction with Knowledge-attention (D19-1)
Copied to clipboard
| Challenge: | Existing attention mechanisms are data-driven, but most are data driven. |
| Approach: | They propose a knowledge-attention encoder which integrates prior knowledge from external lexical resources into deep neural networks for relation extraction task. |
| Outcome: | The proposed system outperforms existing CNN, RNN, and self-attention based models on a large-scale relation extraction dataset. |
Attention Guided Graph Convolutional Networks for Relation Extraction (P19-1)
Copied to clipboard
| Challenge: | Existing approaches for selecting relevant partial dependency structures may not yield optimal results. |
| Approach: | They propose a model which directly takes full dependency trees as inputs and uses them to selectively attend to relevant sub-structures. |
| Outcome: | The proposed model can be understood as a soft-pruning approach that automatically learns how to selectively attend to the relevant sub-structures useful for the relation extraction task. |
HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to extract information graphs are difficult to scale to datasets with longer input texts because of their secondorder space/time complexities. |
| Approach: | They propose a Hybrid SPan GenerAtor that invertibly maps the information graph to an alternating sequence of nodes and edge types and generates them via a hybrid span decoder. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the ACE05 dataset. |
Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extraction (2022.lrec-1)
Copied to clipboard
| Challenge: | Recent research shows that prompt-based learning improves performance on relation extraction tasks. |
| Approach: | They propose a prompt-based learning method that generates comprehensive prompts for biomedical relation extraction using a ChemProt dataset. |
| Outcome: | The proposed method improves fine-tuning on a biomedical relation extraction task with a cloze-test task and fewer training examples to make reasonable predictions. |
RE2: Region-Aware Relation Extraction from Visually Rich Documents (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing studies on relation extraction from visually rich documents focus on layout structure and Optical Character Recognition (OCR) results. |
| Approach: | They propose a relation extraction tool that leverages layout structure among entity blocks to improve relation prediction. |
| Outcome: | The proposed model outperforms existing models on a wide range of domains and languages. |
LexRel: Benchmarking Legal Relation Extraction for Chinese Civil Cases (2026.acl-long)
Copied to clipboard
Yida Cai, Ranjuexiao Hu, Huiyuan Xie, Chenyang Li, Yun Liu, Yuxiao Ye, Zhenghao Liu, Weixing Shen, Zhiyuan Liu
| Challenge: | Legal relations are an important analytical framework for dispute resolution in civil cases. |
| Approach: | They propose a comprehensive schema for legal relations in civil cases with hierarchical taxonomy and definitions of arguments. |
| Outcome: | The proposed schema shows that existing LLMs lack the ability to identify civil legal relations and performance improves on downstream tasks. |