A Hierarchical Approach for Joint Extraction of Entities and Relations

Most existing approaches for the extraction of entities and relations face two main challenges: extracting overlapping relations and capturing the interactions between entity and relation extractions. In this paper, we present a novel sequence-to-sequence model with a hierarchical decoder to solve both issues elegantly and efficiently. Specifically, we use the low-level decoder to predict multi-relations and produce a relation vector for each triple. Given this relation vector, the high-level decoder generates two entities associated with the triple. In this manner, we can directly capture the interactions between entity and relation extractions. Moreover, by decomposing two tasks into two decoding phases, the overlapping multi-relations extraction can be naturally separated. Experiments on popular public datasets demonstrate that our model can effectively extract overlapping triples.