.png){kind=icon}
返回
Learning Entailment Relations by Global Graph Structure Optimization
.png){kind=icon}
OA
AI
DOI：10.1162/COLI_a_00085.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Identifying entailment relations between predicates is an important part of applied semantic inference. In this article we propose a global inference algorithm that learns such entailment rules. First, we define a graph structure over predicates that represents entailment relations as directed edges. Then, we use a global transitivity constraint on the graph to learn the optimal set of edges, formulating the optimization problem as an Integer Linear Program. The algorithm is applied in a setting where, given a target concept, the algorithm learns on the fly all entailment rules between predicates that co-occur with this concept. Results show that our global algorithm improves performance over baseline algorithms by more than 10%.
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:
5.3
论文数:
837
被引数:
2.7K
机构
引用论文
分享收藏N-Myc Induces an EZH2-Mediated Transcriptional Program Driving Neuroendocrine Prostate Cancer.png)
Cancer CellOAAI
Cancer CellIF0
OAAI分享收藏
Preschool children learning about the origin of food, on local farms and in the preschool garden
PREAI
分享收藏
查看更多内容