Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data
Wei Ye, Bo Li, Rui Xie, Zhonghao Sheng, Long Chen, Shikun Zhang · 2019
In practical scenario, relation extraction needs to first identify entity pairs that have relation and then assign a correct relation class.However, the number of non-relation entity pairs in context (negative instances) usually far exceeds the others (positive instances), which negatively affects a model's performance.To mitigate this problem, we propose a multitask architecture which jointly trains a model to perform relation identification with crossentropy loss and relation classification with ranking loss.Meanwhile, we observe that a sentence may have multiple entities and relation mentions, and the patterns in which the entities appear in a sentence may contain useful semantic information that can be utilized to distinguish between positive and negative instances.Thus we further incorporate the embeddings of character-wise/word-wise BIO tag from the named entity recognition task into character/word embeddings to enrich the input representation.Experiment results show that our proposed approach can significantly improve the performance of a baseline model with more than 10% absolute increase in F1-score, and outperform the state-of-theart models on ACE 2005 Chinese and English corpus.Moreover, BIO tag embeddings are particularly effective and can be used to improve other models as well.* indicates equal contribution.