Abstract
This paper presents a text mining-based search approach aimed at information retrieval in the Spanish language. For this purpose, a tool has been developed in order to facilitate and automate the analysis and retrieval, allowing the user to apply different analyzers when carrying out a query, to index and delete documents stored in the system and to evaluate the recovery process. To this extent, a dataset consisting in 27 songs has been used as a case study. Different queries have been made to investigate about the best fitting approaches to the Spanish language and their suitability depending on the query text.
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Acknowledgments
This work has been supported by project MOVIURBAN Máquina social para la gestión sostenible de ciudades inteligentes: movilidad urbana, datos abiertos, sensores móviles (SA070U 16). Project cofinanced with Junta Castilla y Leon, Consejera de Educacion and FEDER funds. In addition, the research of Juan Ramos González has been co-financed by the European Social Fund and Junta de Castilla y León (Operational Programme 2014-2020 for Castilla y León, BOCYL EDU/602/2016).
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Ramos-González, J., Martín-Gómez, L. (2019). A Text Mining-Based Approach for Analyzing Information Retrieval in Spanish: Music Data Collection as a Case Study. In: Rodríguez, S., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-319-99608-0_29
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DOI: https://doi.org/10.1007/978-3-319-99608-0_29
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