TY - JOUR AU - Tan, Zhen AU - Zhao, Xiang AU - Wang, Wei AU - Xiao, Weidong PY - 2019/07/17 Y2 - 2024/03/28 TI - Jointly Extracting Multiple Triplets with Multilayer Translation Constraints JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - AAAI Technical Track: Natural Language Processing DO - 10.1609/aaai.v33i01.33017080 UR - https://ojs.aaai.org/index.php/AAAI/article/view/4689 SP - 7080-7087 AB - <p>Triplets extraction is an essential and pivotal step in automatic knowledge base construction, which captures structural information from unstructured text corpus. Conventional extraction models use a pipeline of named entity recognition and relation classification to extract entities and relations, respectively, which ignore the connection between the two tasks. Recently, several neural network-based models were proposed to tackle the problem, and achieved state-of-the-art performance. However, most of them are unable to extract multiple triplets from a single sentence, which are yet commonly seen in real-life scenarios. To close the gap, we propose in this paper a joint neural extraction model for multitriplets, namely, TME, which is capable of adaptively discovering multiple triplets simultaneously in a sentence via ranking with translation mechanism. In experiment, TME exhibits superior performance and achieves an improvement of 37.6% on F1 score over state-of-the-art competitors.</p> ER -