.png){kind=icon}
返回
Joint entity recognition and relation extraction as a multi-head selection problem
.png){kind=icon}
OA
AI
DOI：10.1016/j.eswa.2018.07.032.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
State-of-the-art models for joint entity recognition and relation extraction strongly rely on external natural language processing (NLP) tools such as POS (part-of-speech) taggers and dependency parsers. Thus, the performance of such joint models depends on the quality of the features obtained from these NLP tools. However, these features are not always accurate for various languages and contexts. In this paper, we propose a joint neural model which performs entity recognition and relation extraction simultaneously, without the need of any manually extracted features or the use of any external tool. Specifically, we model the entity recognition task using a CRF (Conditional Random Fields) layer and the relation extraction task as a multi-head selection problem (i.e., potentially identify multiple relations for each entity). We present an extensive experimental setup, to demonstrate the effectiveness of our method using datasets from various contexts (i.e., news, biomedical, real estate) and languages (i.e., English, Dutch). Our model outperforms the previous neural models that use automatically extracted features, while it performs within a reasonable margin of feature-based neural models, or even beats them. (C) 2018 Elsevier Ltd. All rights reserved.
Keyword:
Entity recognition
Relation extraction
Multi-head selection
Joint model
Sequence labeling
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
Joint entity and relation extraction based on a hybrid neural network基于混合神经网络的联合实体和关系抽取.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Hypothalamic-Pituitary-Adrenal Axis Dysfunction in Alzheimer's Disease: Lack of Association Between Longitudinal and Cross-Sectional Findings阿尔茨海默氏病的下丘脑-垂体-肾上腺轴功能障碍: 纵向和横断面发现之间缺乏关联
PREAI
分享收藏分享收藏 没有更多内容
没有更多内容