This repo covers almost all the papers (35) related to Neural Relation Extraction in ACL, EMNLP, COLING, NAACL, AAAI, IJCAI in 2019.
Use tags to search papers you like.
tags: DSRE | PGM | Combining Direct Supervision | GNN | new perspective | new dataset | joint extraction of relations and entities | ...
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Structured Minimally Supervised Learning for Neural Relation Extraction Fan Bai and Alan Ritter NAACL 2019 paper code
PGM | DSRE
This paper adds a PGM inference into training stage.
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Combining Distant and Direct Supervision for Neural Relation Extraction Iz Beltagy, Kyle Lo and Waleed Ammar NAACL 2019 paper code
Combining Direct Supervision | DSRE
This paper combines direct supervision and distant supervision. It innovatively uses direct supervision for training sigmoid attention in a multi-task way. Further, when applying to the CNN backbone with different filter sizes, adding entity embedding as additional inputs is a useful trick, which performs comparable to RESIDE and better than PCNN-ATT. After combining the supervised sigmoid attention, this paper become a new sota.
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Graph Neural Networks with Generated Parameters for Relation Hao Zhu and Yankai Lin and Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun ACL 2019 paper
GNN | new task | new perspective
This paper considers multi-hop relation extraction, which constructs a fully-connected graph for all entities in a sentence. Experiments show that modeling entity-relation as a graph signifcantly improves the performance.
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Entity-Relation Extraction as Multi-turn Question Answering Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li Arianna Yuan, Duo Chai, Mingxin Zhou and Jiwei Li ACL2019 paper
| new dataset | new perspective| joint extraction of relations and entities
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 fromthe context. Thismulti-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. 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.