Aiming at the problem that the current general-purpose semantic text similarity calculation methods are difficult to use the semantic information of scientific academic conference data, a semantic similarity calculation algorithm for scientific academic conferences by fusion with domain features is proposed. First, the domain feature information of the conference is obtained through entity recognition and keyword extraction, and it is input into the BERT network as a feature and the conference information. The structure of the Siamese network is used to solve the anisotropy problem of BERT. The output of the network is pooled and normalized, and finally the cosine similarity is used to calculate the similarity between the two sessions. Experimental results show that the SBFD algorithm has achieved good results on different data sets, and the Spearman correlation coefficient has a certain improvement compared with the comparison algorithm.

通过实体识别和关键词提取获取会议的领域特征，并将其作为特征和会议信息输入到BERT网络中，使用孪生网络结构解决BERT的各向异性问题，最终使用余弦相似性计算两个会议之间的相似性。实验结果表明，在不同数据集上，SBFD算法取得了良好的结果，并与对比算法相比，Spearman相关系数有一定的提高。

使用领域特征融合的科学学术会议语义相似度计算