Joint Entity and Relation Extraction for Long Text.

Extracting relation triplets from unstructured text has been well studied in recent years. However, previous works focus on solving the relation overlapping problem, and few of them deal with the long text relation extraction. In this work, we introduce a novel end-to-end joint entity and relation extraction model, namely, LTRel, which is capable of extracting relation triplets from long text based on a cross-sentence relation classification algorithm. On the other hand, due to the importance of entity recognition to the entire end-to-end model, we refine the entity tagging scheme and the feature representation of TPLinker, which save the memory space and computation, and also improve the accuracy. We evaluate our model on two public datasets: the English dataset NYT and the Chinese dataset DuIE2.0 proposed by Baidu, both of which are better than state-of-the-art on F1 score, especially significant on the Chinese dataset with a higher proportion of long text samples.