OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction (2020.emnlp-main)
Copied to clipboard
| Challenge: | OpenIE generates extractions iteratively, requiring repeated encoding of partial outputs. |
| Approach: | They propose an iterative open information extraction system that generates extractions iterativly, requiring repeated encoding of partial outputs. |
| Outcome: | The proposed system beats the previous systems by as much as 4 pts in F1 while being much faster. |
Similar Papers
CompactIE: Compact Facts in Open Information Extraction (2022.naacl-main)
Copied to clipboard
| Challenge: | Despite advances in open information extraction, many systems focus on covering more information over compactness of constituents. |
| Approach: | They propose a neural OpenIE system that produces compact extractions with overlapping constituents by using a pipelined approach. |
| Outcome: | The proposed system produces 1.5x-2x more compact extractions than previous systems, with high precision, establishing a new state-of-the-art in OpenIE. |
MILIE: Modular & Iterative Multilingual Open Information Extraction (2022.acl-long)
Copied to clipboard
Bhushan Kotnis, Kiril Gashteovski, Daniel Rubio, Ammar Shaker, Vanesa Rodriguez-Tembras, Makoto Takamoto, Mathias Niepert, Carolin Lawrence
| Challenge: | Current OpenIE systems extract all triple slots independently. |
| Approach: | They propose a neural OpenIE system that extracts triple slots iteratively . they propose to use the system to extract easy slots and difficult ones . |
| Outcome: | The proposed system outperforms SOTA systems on multiple languages ranging from Chinese to Arabic. |
Open Information Extraction from Conjunctive Sentences (C18-1)
Copied to clipboard
| Challenge: | Recent work has highlighted the lack of proper conjunction processing as the most significant source of missed yield in Open IE. |
| Approach: | They develop a coordination analyzer that searches over hierarchical conjunct boundaries and uses a language model to score conjunctions. |
| Outcome: | The proposed system performs extraction over the simple sentences identified by CALM to obtain up to 1.8x yield with a moderate increase in precision compared to extractions from original sentences. |
Open Information Extraction with Entity Focused Constraints (2023.findings-eacl)
Copied to clipboard
| Challenge: | Open Information Extraction (OIE) is the task of extracting tuples from unstructured corpora without any knowledge of the type and lexical form of the subject, the object, or the subject. |
| Approach: | They exploit domain knowledge to inject constraints into the extraction through constrained inference and constraint-aware training. |
| Outcome: | The proposed approach improves the CaRB and WIRe57 metric and achieves a 29.17% improvement in the CARB and 24.37% improvement on the WIRe56 metric. |
Syntactic Multi-view Learning for Open Information Extraction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Open Information Extraction (OpenIE) aims to generate structured tuples from unstructured open-domain text. |
| Approach: | They propose to model constituency and dependency trees into word-level graphs and combine them with sentential semantic representations to extract relational tuples. |
| Outcome: | The proposed model integrates constituency and dependency trees into word-level graphs and enables neural OpenIE to learn from syntactic structures. |
Guide the Many-to-One Assignment: Open Information Extraction via IoU-aware Optimal Transport (2023.acl-long)
Copied to clipboard
Kaiwen Wei, Yiran Yang, Li Jin, Xian Sun, Zequn Zhang, Jingyuan Zhang, Xiao Li, Linhao Zhang, Jintao Liu, Guo Zhi
| Challenge: | Open Information Extraction (OIE) aims to extract structured information from text without the limitations of close ontology. |
| Approach: | They propose a method to assign ground truth labels to parallelly generated tuple proposals . they leverage intersection-over-union (IoU) as assignment quality measurement . |
| Outcome: | The proposed method outperforms the state-of-the-art models on three benchmarks. |
IMoJIE: Iterative Memory-Based Joint Open Information Extraction (2020.acl-main)
Copied to clipboard
| Challenge: | Recent neural OpenIE systems are statistical or rule-based for Open Information Extraction. |
| Approach: | They propose an extension to CopyAttention that produces the next extraction conditioned on all previously extracted tuples. |
| Outcome: | The proposed model outperforms CopyAttention by 18 pts and a BERT-based strong baseline by 2 ptes. |
A Survey on Open Information Extraction from Rule-based Model to Large Language Model (2024.findings-emnlp)
Copied to clipboard
Liu Pai, Wenyang Gao, Wenjie Dong, Lin Ai, Ziwei Gong, Songfang Huang, Li Zongsheng, Ehsan Hoque, Julia Hirschberg, Yue Zhang
| Challenge: | Open Information Extraction (OpenIE) is a key NLP task aimed at extracting structured information from unstructured text sources. |
| Approach: | They propose to categorize OpenIE into rule-based, neural, and pre-trained large language models and discuss each within a chronological framework. |
| Outcome: | The paper categorizes OpenIE approaches into rule-based, neural, and pre-trained large language models, discussing each within a chronological framework. |
Systematic Comparison of Neural Architectures and Training Approaches for Open Information Extraction (2020.emnlp-main)
Copied to clipboard
| Challenge: | Open information extraction (OIE) is a method for extracting facts from text in structured format . alternative formulations allow for longer tuples, but most work focuses on binary predicates only. |
| Approach: | They propose to extract facts from natural language text and represent them as structured triples . they compare different neural network architectures and training approaches . |
| Outcome: | The proposed approach improves the currently best models on the OIE16 benchmark by 0.421 F1 score and 0.420 AUC-PR . |
CycleOIE: A Low-Resource Training Framework For Open Information Extraction (2025.coling-main)
Copied to clipboard
Zhihong Jin, Chunhong Zhang, Zheng Hu, Jibin Yu, Ruiqi Ma, Qingyun Chen, Xiaohao Liao, Yanxing Zhang
| Challenge: | Open Information Extraction (OpenIE) models rely heavily on large amounts of annotated data. |
| Approach: | They propose a training framework that maximizes data efficiency through a cycle-consistency mechanism. |
| Outcome: | The proposed approach improves the quality of training data by curating low-quality datasets annotated by a large language model. |