DocOIE: A Document-level Context-Aware Dataset for OpenIE

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 2377–2389 August 1–6, 2021. ©2021 Association for Computational Linguistics 2377 DocOIE: A Document-level Context-Aware Dataset for OpenIE Kuicai Dong1, Yilin Zhao1, Aixin Sun1, Jung-Jae Kim2, Xiaoli Li1,2 1 School of Computer Science and Engineering, Nanyang Technological University, Singapore {kuicai001, zhao0320}@e.ntu.edu.sg, {axsun, xlli}@ntu.edu.sg 2 Institute for Infocomm Research, A*STAR, Singapore {jjkim, xlli}@i2r.a-star.edu.sg Abstract Open Information Extraction (OpenIE) aims to extract structured relational tuples (subject, relation, object) from sentences, and plays a critical role in many NLP applications. Ex- isting solutions perform extraction at sentence level, without referring to any additional con- textual information. In reality, however, a sen- tence typically exists as part of a document rather than standalone; we often need to access relevant contextual information around the sen- tence before we can accurately interpret it. As there is no document-level context-aware Ope- nIE dataset available, we manually annotate 800 sentences from 80 documents in two do- mains (Healthcare and Transportation) to form a DocOIE dataset for evaluation. In addition, we propose DocIE, a document-level context- aware OpenIE model. Our experimental re- sults demonstrate that incorporating document- level context is helpful in improving OpenIE performance. Both the DocOIE dataset and DocIE model are available online.1 1 Introduction Open Information Extraction has been a critical NLP task as it can extract structured relational tuples (subject, relation, object) from unstruc- tured text. The OpenIE system is fully domain- independent, and does not need input from users. It is also highly scalable and allows fast querying mechanism (Yates et al., 2007). Therefore, Ope- nIE has been successfully applied to a variety of downstream NLP tasks, such as knowledge base population (Mart´ınez-Rodr´ıguez et al., 2018; Gash- teovski et al., 2020), question answering (Khot et al., 2017), and summarization (Fan et al., 2019). Current OpenIE methods mainly focus on ex- tracting tuples at sentence level. However, in many NLP scenarios, sentences exist as part of a docu- ment rather than standalone. Given a document 1https://github.com/daviddongkc/DocOIE Sentence 1 Data transfers to a single target terminal using the invention might not be significantly faster than conventional download methods. (Pat No. 8495167) (data; transfers to; a single target terminal) (data transfers to a single target terminal; use; the invention) Context S1: Data security is improved as compared with transferring plain text and data transfer requires less time. Context S2: If a new terminal is registered to the main server during the transfer it will be included in the next data transfer. Context S3: Examples of data transfers will be described with reference to a preferred embodiment of a network. Sentence 2 Node-B can be a device a cellular base station having beam-forming antennas that serves various sectors of a cell. (Pat No. 8160027) (node-B; can be; a device a cellular base station) (node-B; can be; a device) (a device, is such as, a cellular base station) Context S4: A Node-B can be a device, such as, a cellular base station that serves an entire cell. Figure 1: Example …

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