基于BERTCA的新闻实体与正文语义相关度计算模型

目前的搜索引擎仍然存在“重形式,轻语义”的问题,无法做到对搜索关键词和文本的深层次语义理解,因此语义检索成为当前搜索引擎中亟需解决的问题。为了提高搜索引擎的语义理解能力,该文提出一种语义相关度的计算方法。首先,标注了金融类新闻标题实体与新闻正文语义相关度语料1万条,然后建立新闻实体与正文语义相关度计算的BERTCA(Bidirectional Encoder Representation from Transformers Co-Attention)模型,通过使用BERT预训练模型,综合考虑细粒度的实体和粗粒度的正文的语义信息,然后经过协同注意力,实现实体与正文的语义匹配,不仅能计算出金融新闻实体与新闻正文之间的相关度,还能根据相关度阈值来判定相关度类别,实验表明该模型在1万条标注语料上准确率超过95%,优于目前主流模型,最后通过具体搜索示例展示了该模型的优秀性能。

BERTCA Based Semantic Relevance Model for News Entity and Text

In order to improve the ability of semantic retrieval in search engines, this paper proposes a semantic relevancy model for news entity and text. A corpus 10, 000 financial news with the semantic relatedness between entities in headlines and text has been manually annotated. Then the BERTCA (Bidirectional Encoder Representation from Transformers Co-Attention semantic relevancy computing) model has been established using this corpus. Through the co-attention mechanism, this model can obtain the semantic matching between the entity and text, and it can not only calculate the degree of correlation between entity and text, but also determine the degree of correlation according to the semantic relevancy. The experimental results show that the accuracy of the proposed model surpasses 95%, which is better than the state-of-the-art models.