چکیده (انگلیسی):

Measuring cross-language semantic similarity between short
texts is a task that is challenging in terms of human understanding. This
paper addresses this problem by carrying out a study of Arabic–English
semantic similarity in short phrases and sentences. Human-rated benchmark
dataset was carefully constructed for this research. Dictionary and machine
translation techniques were employed to determine the relatedness between
the cross-lingual texts from a monolingual perspective. Three algorithms
were developed to rate the semantic similarity and these were applied to the
human-rated benchmark. An averaged maximum-translation similarity
algorithm was proposed using the term sets produced by the dictionarybased
technique. Noun-verb and term vectors obtained by the Machine
Translation (MT) technique were also suggested to compute the semantic
similarity. The results were compared with the human ratings in our
benchmark using Pearson correlation coefficient and these were
triangulated with the best, worst and mean for all human participants. MTbased
term vector semantic similarity algorithm obtained the highest
correlation (r = 0.8657) followed by averaged maximum-translation
similarity algorithm (r = 0.7206). Further statistical analysis showed no
significant difference between both algorithms and the humans’ judgement.