DocOIE: A Document-level Context-Aware Dataset for OpenIE
- URL: http://arxiv.org/abs/2105.04271v2
- Date: Tue, 11 May 2021 01:49:59 GMT
- Title: DocOIE: A Document-level Context-Aware Dataset for OpenIE
- Authors: Kuicai Dong, Yilin Zhao, Aixin Sun, Jung-Jae Kim, Xiaoli Li
- Abstract summary: Open Information Extraction (OpenIE) aims to extract structured relationals from sentences.
Existing solutions perform extraction at sentence level, without referring to any additional contextual information.
We propose DocIE, a novel document-level context-aware OpenIE model.
- Score: 22.544165148622422
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Open Information Extraction (OpenIE) aims to extract structured relational
tuples (subject, relation, object) from sentences and plays critical roles for
many downstream NLP applications. Existing solutions perform extraction at
sentence level, without referring to any additional contextual information. In
reality, however, a sentence typically exists as part of a document rather than
standalone; we often need to access relevant contextual information around the
sentence before we can accurately interpret it. As there is no document-level
context-aware OpenIE dataset available, we manually annotate 800 sentences from
80 documents in two domains (Healthcare and Transportation) to form a DocOIE
dataset for evaluation. In addition, we propose DocIE, a novel document-level
context-aware OpenIE model. Our experimental results based on DocIE demonstrate
that incorporating document-level context is helpful in improving OpenIE
performance. Both DocOIE dataset and DocIE model are released for public.
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