Relational extraction is one of the basic tasks related to information
extraction in the field of natural language processing, and is an important
link and core task in the fields of information extraction, natural language
understanding, and information retrieval. None of the existing relation
extraction methods can effectively solve the problem of triple overlap. The
CasAug model proposed in this paper based on the CasRel framework combined with
the semantic enhancement mechanism can solve this problem to a certain extent.
The CasAug model enhances the semantics of the identified possible subjects by
adding a semantic enhancement mechanism, First, based on the semantic coding of
possible subjects, pre-classify the possible subjects, and then combine the
subject lexicon to calculate the semantic similarity to obtain the similar
vocabulary of possible subjects. According to the similar vocabulary obtained,
each word in different relations is calculated through the attention mechanism.
For the contribution of the possible subject, finally combine the relationship
pre-classification results to weight the enhanced semantics of each
relationship to find the enhanced semantics of the possible subject, and send
the enhanced semantics combined with the possible subject to the object and
relationship extraction module. Complete the final relation triplet extraction.
The experimental results show that, compared with the baseline model, the
CasAug model proposed in this paper has improved the effect of relation
extraction, and CasAug's ability to deal with overlapping problems and extract
multiple relations is also better than the baseline model, indicating that the
semantic enhancement mechanism proposed in this paper It can further reduce the
judgment of redundant relations and alleviate the problem of triple overlap.

通过结合语义增强机制，本论文提出的 CasAug 模型能够解决关系抽取中的三元组重叠问题，提高关系抽取效果，并能更好地处理重叠问题和提取多个关系。