基于深度学习的实体关系抽取研究综述(Review of Entity Relation Extraction based on deep learning)

no code implementations • CCL 2020 • Zhentao Xia, Weiguang Qu, Yanhui Gu, Junsheng Zhou, Bin Li

作为信息抽取的一项核心子任务, 实体关系抽取对于知识图谱、智能问答、语义搜索等自然语言处理应用都十分重要。关系抽取在于从非结构化文本中自动地识别实体之间具有的某种语义关系。该文聚焦句子级别的关系抽取研究, 介绍用于关系抽取的主要数据集并对现有的技术作了阐述, 主要分为:有监督的关系抽取、远程监督的关系抽取和实体关系联合抽取。我们对比用于该任务的各种模型, 分析它们的贡献与缺 陷。最后介绍中文实体关系抽取的研究现状和方法。

Relation Extraction

Neural Network based Deep Transfer Learning for Cross-domain Dependency Parsing

no code implementations • 8 Aug 2019 • Zhentao Xia, Likai Wang, Weiguang Qu, Junsheng Zhou, Yanhui Gu

In this paper, we describe the details of the neural dependency parser sub-mitted by our team to the NLPCC 2019 Shared Task of Semi-supervised do-main adaptation subtask on Cross-domain Dependency Parsing.

Dependency Parsing Transfer Learning