Abstract
We consider the problem of building a content-based recommender/filtering system in a parliamentary context which, given a new document to be recommended, can decide those Members of Parliament who should receive it. We propose and compare two different approaches to tackle this task, namely a machine learning-based method using automatic document classification and an information retrieval-based approach that matches documents and legislators’ representations. The information necessary to build the system is automatically extracted from the transcriptions of the speeches of the members of parliament within the parliament debates. Our proposals are experimentally tested for the case of the regional Andalusian Parliament at Spain.
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Acknowledgements
This work has been funded by the Spanish “Ministerio de Economía y Competitividad” under projects TIN2013-42741-P and TIN2016-77902-C3-2-P, and the European Regional Development Fund (ERDF-FEDER).
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de Campos, L.M., Fernández-Luna, J.M., Huete, J.F., Redondo-Expósito, L. (2017). Comparing Machine Learning and Information Retrieval-Based Approaches for Filtering Documents in a Parliamentary Setting. In: Moral, S., Pivert, O., Sánchez, D., Marín, N. (eds) Scalable Uncertainty Management. SUM 2017. Lecture Notes in Computer Science(), vol 10564. Springer, Cham. https://doi.org/10.1007/978-3-319-67582-4_5
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