Relation Extraction in 2019

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 | ...

NAACL 2019

1. 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.

2. 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.

ACL 2019

1. 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.

2. 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.