| Challenge: | Open Information Extraction (Open IE) systems have been evaluated traditionally via manual annotation. |
| Approach: | They propose to use a dataset to score Open IE systems by matching system predictions with benchmark datasets. |
| Outcome: | The proposed framework matches predictions with the benchmark dataset and is noisy and inconsistent. |
Similar Papers
BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation (2022.acl-long)
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| Challenge: | Existing benchmarks for OIE are incomplete and do not include all acceptable variants of the same fact. |
| Approach: | They introduce BenchIE: a benchmark and evaluation framework for comprehensive evaluation of OIE systems for English, Chinese, and German. |
| Outcome: | The proposed framework is based on fact synsets, clusters, and standardized benchmarks. |
Improving Open Information Extraction via Iterative Rank-Aware Learning (P19-1)
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| Challenge: | Open information extraction (IE) is the task of extracting open-domain assertions from natural language sentences. |
| Approach: | They propose an additional binary classification loss to calibrate the extraction likelihood . they propose an iterative learning process where extractions generated by the open IE model are incrementally included as training samples to help the model learn from trial and error. |
| Outcome: | Experiments on open information extraction (IE) show that the extraction likelihood is not well calibrated when comparing quality of extracted assertions. |
Syntactically Rich Discriminative Training: An Effective Method for Open Information Extraction (2022.emnlp-main)
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| Challenge: | Open information extraction (OIE) is the task of extracting facts from natural language text. |
| Approach: | They propose a method for computing syntactically rich text embeddings using the structure of dependency trees and a discriminative approach to OIE where tokens in the generated fact are classified as "real" and "fake" they propose to reduce repetitive tokens and improve models' ability to generate implicit facts by a factor of 23%. |
| Outcome: | The proposed method reduces repetitive tokens by a factor of 23% on the CaRB, OIE2016, and LSOIE datasets and improves on augmented datasets. |
A Survey on Open Information Extraction (C18-1)
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| Challenge: | Existing approaches to open information extraction (Open IE) focus on narrow, well-defined requests over a predefined set of target relations on small, homogeneous corpora. |
| Approach: | They propose to use unsupervised methods to extract all types of relations found in text . they propose to implement a system that can be automated to detect possible relations . |
| Outcome: | The proposed approaches have been compared with existing methods and are based on the results of a literature review. |
IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models (2022.emnlp-main)
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| Challenge: | Recent studies show pre-trained LMs store linguistic and relational knowledge . pre-training LM models can answer "fill-in-the-blank" questions based on pre-defined relations . |
| Approach: | They propose an open information extraction benchmark for pre-trained language models . they turn pre-trained LMs into zero-shot OIE systems to examine open relational information . |
| Outcome: | The proposed benchmark outperforms state-of-the-art methods on factual OIE datasets without training sets. |
BenchIE^FL: A Manually Re-Annotated Fact-Based Open Information Extraction Benchmark (2024.findings-acl)
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| Challenge: | Numerous OIE systems claim ever-increasing performance, limiting their effectiveness. |
| Approach: | They propose a benchmark which fully enforces the principles of BenchIE while containing fewer errors, omissions and shortcomings when candidate facts are matched towards reference ones. |
| Outcome: | The proposed benchmark aims to improve the performance of OIE extractors while avoiding errors and omissions. |
ADAPTIVE IE: Investigating the Complementarity of Human-AI Collaboration to Adaptively Extract Information on-the-fly (2025.coling-main)
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Ishani Mondal, Michelle Yuan, Anandhavelu N, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek, Benjamin Van Durme, Jordan Boyd-Graber
| Challenge: | Existing IE systems are either fully supervised, requiring expensive human annotations, or fully unsupervised, extracting information that often do not cater to user’s needs. |
| Approach: | They propose a framework that uses human-in-the-loop refinement to adapt to changing user questions. |
| Outcome: | The proposed framework is domain-agnostic, responsive, efficient for helping users access useful information while quickly reorganizing information in response to evolving information needs. |
Systematic Comparison of Neural Architectures and Training Approaches for Open Information Extraction (2020.emnlp-main)
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| 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 . |
AnnIE: An Annotation Platform for Constructing Complete Open Information Extraction Benchmark (2022.acl-demo)
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Niklas Friedrich, Kiril Gashteovski, Mingying Yu, Bhushan Kotnis, Carolin Lawrence, Mathias Niepert, Goran Glavaš
| Challenge: | Open Information Extraction (OIE) is the task of extracting facts from sentences in the form of relations and their corresponding arguments in schema-free manner. |
| Approach: | They propose an interactive annotation platform that facilitates annotating complete facts from input sentences. |
| Outcome: | The proposed platform facilitates such challenging annotation tasks and supports creation of fact-oriented OIE evaluation benchmarks. |
LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction (2021.eacl-main)
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| Challenge: | Open Information Extraction (OIE) systems extract factual propositions into n-ary tuples . current datasets are limited in size and diversity . |
| Approach: | They propose to convert QA-SRL 2.0 dataset to large-scale OIE dataset LSOIE. |
| Outcome: | The proposed dataset is 20 times larger than the next largest human-annotated OIE dataset. |