分类:The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction

来自Big Physics
Qiaoxizi（讨论 | 贡献）2020年12月13日 (日) 09:37的版本 （→‎总结和评论）


Waleed Ammar, Matthew E. Peters, Chandra Bhagavatula, R. Power, The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction

Abstract

This paper describes our submission for the ScienceIE shared task (SemEval2017 Task 10) on entity and relation extraction from scientific papers. Our model is based on the end-to-end relation extraction model of Miwa and Bansal (2016) with several enhancements such as semi-supervised learning via neural language models, character-level encoding, gazetteers extracted from existing knowledge bases, and model ensembles. Our official submission ranked first in end-to-end entity and relation extraction (scenario 1), and second in the relation-only extraction (scenario 3).

总结和评论

这篇文章基于对经典的end-to-end 实体关系联合抽取模型（见论文End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures）的改进，实现了一些科学论文中的概念和关系提取算法。

  1. 任务：

从科学论文中识别实体（Task / Material / Process）以及抽取实体关系（ Hyponym-of / Synonym-of）

概念地图

本分类目前不含有任何页面或媒体文件。

取自“https://www.bigphysics.org/index.php?title=分类:The_AI2_system_at_SemEval-2017_Task_10_(ScienceIE):_semi-supervised_end-to-end_entity_and_relation_extraction&oldid=5236”