Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detection
This addresses a critical issue for downstream tasks that rely on OIE tuples as facts, potentially improving reliability in applications like knowledge base construction, but it is incremental as it builds on existing OIE and speculation detection work.
The paper tackles the problem of uncertainty in Open Information Extraction (OIE) by proposing tuple-level speculation detection to determine if extracted relational tuples are speculative, rather than assuming all tuples are factual, and introduces a baseline model OIE-Spec for this task.
Open Information Extraction (OIE) aims to extract factual relational tuples from open-domain sentences. Downstream tasks use the extracted OIE tuples as facts, without examining the certainty of these facts. However, uncertainty/speculation is a common linguistic phenomenon. Existing studies on speculation detection are defined at sentence level, but even if a sentence is determined to be speculative, not all tuples extracted from it may be speculative. In this paper, we propose to study speculations in OIE and aim to determine whether an extracted tuple is speculative. We formally define the research problem of tuple-level speculation detection and conduct a detailed data analysis on the LSOIE dataset which contains labels for speculative tuples. Lastly, we propose a baseline model OIE-Spec for this new research task.