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Semantic similarity aggregators for very short textual expressions: a case study on landmarks and points of interest

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Abstract

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.

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Acknowledgments

We would like to thank the anonymous reviewers for their helpful and constructive comments that greatly contributed to improve this work. The research reported in this paper has been supported by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry of Science, Research and Economy, and the Province of Upper Austria under the frame of the COMET Center SCCH [FFG: 844597].

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  1. Software Competence Center Hagenberg GmbH, Softwarepark 21, 4232, Hagenberg, Austria

    Jorge Martinez-Gil

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Martinez-Gil, J. Semantic similarity aggregators for very short textual expressions: a case study on landmarks and points of interest. J Intell Inf Syst 53, 361–380 (2019). https://doi.org/10.1007/s10844-019-00561-0

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