Extracting entities and relations from unstructured text has attracted increasing attention in recent
    years but remains challenging, due to the intrinsic difficulty in identifying overlapping relations
    with shared entities.

    TPLinker formulates joint extraction as a token pair linking problem

    Experiment results show that TPLinker performs significantly better on
    overlapping and multiple relation extraction, and achieves state-of-the-art performance on two
    public datasets.

    To mitigate the issue

    Extracting entities and relations from unstructured texts is an essential step in automatic knowledge base
    construction.

    Traditional pipelined approaches
    However, due to the complete separation of entity detection and relation classification, these models ignore the interaction and correlation between
    the two subtasks, being susceptible to cascading errors.

    building joint models

    利用：employ；exploit；integrate；adopt

    减轻：alleviate；To break the bottleneck of manual labeling

    These methods in-
    troduce exposure bias, which may cause the
    models overfit to the frequent label combina-
    tion, thus limiting the generalization ability.