Time-DS is composed of a time series and two strategies. Instance-popularity is to encode the strong relevance of time and true relation mention. The two strategies, i.e., hard filter and curriculum learning are both ways to implement instance-pop popularity for better relation extraction.
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.