@techreport{jiang2019towards,
author = {Jiang, Tianwen and Zhao, Sendong and Liu, Jing and Yao, Jin-Ge and Liu, Ming and Qin, Bing and Liu, Ting and Lin, Chin-Yew},
title = {Towards Time-Aware Distant Supervision for Relation Extraction.},
institution = {Microsoft},
year = {2019},
month = {March},
abstract = {Distant supervision for relation extraction heavily suffers from the wrong labeling problem. To alleviate this issue in news data with the timestamp, we take a new factor time into consideration and propose a novel time-aware distant supervision framework (Time-DS). Time-DS is composed of a time series instance-popularity and two strategies. Instance-popularity is to encode the strong relevance of time and true relation mention. Therefore, instance-popularity would be an effective clue to reduce the noises generated through distant supervision labeling. The two strategies, i.e., hard filter and curriculum learning are both ways to implement instance-popularity for better relation extraction in the manner of Time-DS. The curriculum learning is a more sophisticated and flexible way to exploit instance-popularity to eliminate the bad effects of noises, thus get better relation extraction performance. Experiments on our collected multi-source news corpus show that Time-DS achieves significant improvements for relation extraction.},
url = {http://approjects.co.za/?big=en-us/research/publication/towards-time-aware-distant-supervision-for-relation-extraction/},
number = {MSR-TR-2019-43},
}