Semantic similarity aggregators for very short textual expressions: a case study on landmarks and points of interest
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Semantic similarity measurement aims to automatically compute the degree of similarity between two textual expressions that use different representations for naming the same concepts. However, very short textual expressions cannot always follow the syntax of a written language and, in general, do not provide enough information to support proper analysis. This means that in some fields, such as the processing of landmarks and points of interest, results are not entirely satisfactory. In order to overcome this situation, we explore the idea of aggregating existing methods by means of two novel aggregation operators aiming to model an appropriate interaction between the similarity measures. As a result, we have been able to improve the results of existing techniques when solving the GeReSiD and the SDTS, two of the most popular benchmark datasets for dealing with geographical information.