论文标题
与域特征融合的科学学术会议的语义相似性计算
Semantic Similarity Computing for Scientific Academic Conferences fused with domain features
论文作者
论文摘要
目的是针对当前通用语义文本相似性计算方法的问题很难使用科学学术会议数据的语义信息，因此提出了通过融合与域特征的科学学术会议的语义相似性计算算法。首先，会议的域功能信息是通过实体识别和关键字提取获得的，并将其作为功能和会议信息输入到BERT网络中。暹罗网络的结构用于解决BERT的各向异性问题。网络的输出汇总并归一化，最后使用余弦相似性来计算两个会话之间的相似性。实验结果表明，SBFD算法在不同的数据集上取得了良好的结果，而Spearman相关系数与比较算法相比具有一定的改进。
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.
