FREDA: Flexible Relation Extraction Data Annotation
This addresses the challenge of data annotation for Relation Extraction, which is time-consuming and often sacrifices accuracy, by providing a flexible method for researchers and practitioners.
The paper tackles the problem of efficiently creating high-quality datasets for Relation Extraction by proposing FREDA, which annotated 10,022 sentences for 19 relations quickly and achieved very good results with neural models that generalize well.
To effectively train accurate Relation Extraction models, sufficient and properly labeled data is required. Adequately labeled data is difficult to obtain and annotating such data is a tricky undertaking. Previous works have shown that either accuracy has to be sacrificed or the task is extremely time-consuming, if done accurately. We are proposing an approach in order to produce high-quality datasets for the task of Relation Extraction quickly. Neural models, trained to do Relation Extraction on the created datasets, achieve very good results and generalize well to other datasets. In our study, we were able to annotate 10,022 sentences for 19 relations in a reasonable amount of time, and trained a commonly used baseline model for each relation.