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Entity-Relation Extraction as Multi-Turn Question Answering
- Source :
- ACL (1)
- Publication Year :
- 2019
- Publisher :
- arXiv, 2019.
-
Abstract
- In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast the task as a multi-turn question answering problem, i.e., the extraction of entities and relations is transformed to the task of identifying answer spans from the context. This multi-turn QA formalization comes with several key advantages: firstly, the question query encodes important information for the entity/relation class we want to identify; secondly, QA provides a natural way of jointly modeling entity and relation; and thirdly, it allows us to exploit the well developed machine reading comprehension (MRC) models. Experiments on the ACE and the CoNLL04 corpora demonstrate that the proposed paradigm significantly outperforms previous best models. We are able to obtain the state-of-the-art results on all of the ACE04, ACE05 and CoNLL04 datasets, increasing the SOTA results on the three datasets to 49.4 (+1.0), 60.2 (+0.6) and 68.9 (+2.1), respectively. Additionally, we construct a newly developed dataset RESUME in Chinese, which requires multi-step reasoning to construct entity dependencies, as opposed to the single-step dependency extraction in the triplet exaction in previous datasets. The proposed multi-turn QA model also achieves the best performance on the RESUME dataset.<br />Comment: to appear at ACL2019
- Subjects :
- FOS: Computer and information sciences
Class (computer programming)
Computer Science - Computation and Language
Dependency (UML)
Relation (database)
Computer science
business.industry
Context (language use)
02 engineering and technology
Construct (python library)
010501 environmental sciences
computer.software_genre
01 natural sciences
Task (project management)
0202 electrical engineering, electronic engineering, information engineering
Key (cryptography)
Question answering
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Computation and Language (cs.CL)
Natural language processing
0105 earth and related environmental sciences
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- ACL (1)
- Accession number :
- edsair.doi.dedup.....11d8e331d6bbf78f3f260b103cf51bac
- Full Text :
- https://doi.org/10.48550/arxiv.1905.05529