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	Improving First Order Temporal Fact Extraction with Unreliable Data
	Bingfeng Luo ; Yansong Feng ; Zheng Wang ; Dongyan Zhao
	2016
关键词	temporal fact extraction distant supervision knowledge base
英文摘要	In this paper,we deal with the task of extracting first order temporal facts from free text.This task is a subtask of relation extraction and it aims at extracting relations between entity and time.Currently,the field of relation extraction mainly focuses on extracting relations between entities.However,we observe that the multi-granular nature of time expressions can help us divide the dataset constructed by distant supervision into reliable and less reliable subsets,which can help to improve the extraction results on relations between entity and time.We accordingly contribute the first dataset focusing on the first order temporal fact extraction task using distant supervision.To fully utilize both the reliable and the less reliable data,we propose to use curriculum learning to rearrange the training procedure,label dropout to make the model be more conservative about less reliable data,and instance attention to help the model distinguish important instances from unimportant ones.Experiments show that these methods help the model outperform the model trained purely on the reliable dataset as well as the model trained on the dataset where all subsets are mixed together.; 1-12
语种	英语
出处	第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/480617]
专题	计算机科学技术研究所
推荐引用方式 GB/T 7714	Bingfeng Luo,Yansong Feng,Zheng Wang,et al. Improving First Order Temporal Fact Extraction with Unreliable Data. 2016-01-01.

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