  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.
