Disclosed is a method for determining sentence similarity, which belongs to the technical field of natural language processing. The method comprises: according to two externally input sentence samples, acquiring a character/word vector matrix from a sentence sample; extracting overlapped features from the sentence sample to form an overlapped feature matrix, and combining the character/word vector matrix with the overlapped feature matrix so same serve as input data for a first neural network model; carrying out processing according to the first neural network model to obtain a sentence vector for the sentence sample, carrying out an operation to form a sentence combination vector, and combining same with an overlapped feature vector formed according to the overlapped features so same serve as input data for a second neural network model; and carrying out processing according to the second neural network model to obtain a similarity metric and output same so same serves as a basis for determining the similarity of the two sentence samples. The beneficial effect of the technical solution is solving the problem in the prior art of the calculation of sentence similarity relying heavily on the quality of a pre-trained character/word vector and a non-logged word.
METHOD FOR DETERMINING SENTENCE SIMILARITY