Papers with Open Information Extraction
When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications (2023.acl-long)
Copied to clipboard
| Challenge: | Existing consensus on which OpenIE model is best for each application is lacking . different assumptions made by different models and datasets have a statistically significant effect on performance, making it important to choose the most appropriate OpenIE system for one’s applications. |
| Approach: | They propose to use OpenIE to extract relation tuples from plain text to compare different models and training sets to find the best model for their applications. |
| Outcome: | The proposed models perform well on a Complex QA application. |
A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing OIE (Open Information Extraction) algorithms are redundant and not reusable. |
| Approach: | They propose a pipeline where an Open-domain Information eXpression task provides a platform for all OIE strategies. |
| Outcome: | The proposed pipeline provides a platform for all OIE strategies. |
Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain (D18-1)
Copied to clipboard
| Challenge: | Experimental results reveal dual structure between OIE and OIN tasks helps to build better OIE agents and OINE agents. |
| Approach: | They propose an Open-Domain Information Narration task as the reverse task of Open Information Extraction (OIE) they then propose an OIN task as an OIE agent and an OIR agent to implement the dual structure . |
| Outcome: | The proposed task is the reverse task of Open Information Extraction (OIE) The proposed system is able to implement the dual structure with a reinforcement learning paradigm. |
Revisiting the Task of Scoring Open IE Relations (L18-1)
Copied to clipboard
| Challenge: | Recent Open Information Extraction systems allow us to extract ever larger (yet incomplete) open-domain Knowledge Bases from text. |
| Approach: | They propose a baseline model which gives competitive results in a previously defined protocol and provides an independent source of signal to judge arbitrary fact plausibility. |
| Outcome: | The proposed model gives competitive results in the previously defined protocol and provides an independent source of signal to judge arbitrary fact plausibility. |