Text similarity measures play a vital role in text related applications in tasks such as social networks

text mining

natural language processing

and others.The typical characteristics of short texts demonstrate severe sparseness and high dimension while the traditional short texts similarity calculation always ignores category information.A coupled distance discrimination and strong classification features based approach for short text similarity calculation

CDDCF

is presented.On the one hand

co-occurrence distance between terms are considered in each text to determine the co-occurrence distance correlation

based on which the weight for each term can be determined and the intra and inter relations between words are established.The similarity of coupling distance discrimination on short text can be captured.On the other hand

strong classification features are extracted via labeled texts.The similarity between two short texts is measured by using the common number of strong discrimination features with the same context.Finally

the distance discrimination and strong classification features are unified into a joint framework to measure the similarity of short texts.Experimental results show that CDDCF performs better compared to baseline algorithms in term of its performance and efficiency of similarity computation.