期刊论文

Ouyang, Chunping

英文

Expert Systems with Applications

0957-4174

2017

83

114-121

10.1016/j.eswa.2017.04.036

973 ProgramNational Basic Research Program of China [2014CB340504]; State Key Program of National Natural Science of ChinaNational Natural Science Foundation of China (NSFC) [61533018]; NSFC-ANRFrench National Research Agency (ANR) [61261130588]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61402220, 1309007]; State Scholarship Fund of CSC [201608430240]; Philosophy and Social Science Foundation of Hunan Province [16YBA323]; Tsinghua University Initiative Scientific Research Program [20131089256]; Science and Technology Support Program [2014BAK04B00]; China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2014M550733]; THU-NUS NExT Co-Lab; Scientific Research Fund of Hunan Provincial Education DepartmentHunan Provincial Education Department [16C1378]

本校为第一且通讯机构

Joint inference is a fundamental issue in the field of artificial intelligence. The greatest advantage of the joint inference is demonstrated by its capability of avoiding errors from cascading and accumulating on a pipeline of multiple chained sub-tasks. Markov Logic Network(MLN) is the most common joint inference model that provides a flexible representation and handles uncertainty. It has been applied successfully to joint inference on many natural language processing tasks to avoid error propagation. However, due to the great expressiveness...