Semi-Open Information Extraction

作者：Bowen Yu, Zhenyu Zhang, Jiawei Sheng, Tingwen Liu, Yubin Wang, Yucheng Wang, Bin Wang · 年份：2021 · DOI：10.1145/3442381.3450029 · 被引用次数：30 · 研究领域：Topic Modeling、Natural Language Processing Techniques、Advanced Text Analysis Techniques

Open Information Extraction (OIE), the task aimed at discovering all textual facts organized in the form of (subject, predicate, object) found within a sentence, has gained much attention recently. However, in some knowledge-driven applications such as question answering, we often have a target entity and hope to obtain its structured factual knowledge for better understanding, instead of extracting all possible facts aimlessly from the corpus. In this paper, we define a new task, namely Semi-Open Information Extraction (SOIE), to address this need. The goal of SOIE is to discover domain-independent facts towards a particular entity from general and diverse web text. To facilitate research on this new task, we propose a large-scale human-annotated benchmark called SOIED, consisting of 61,984 facts for 8,013 subject entities annotated on 24,000 Chinese sentences collected from the web search engine.