Abstract
In this paper we proposed a novel Deep Learning approach to realize a Word Embeddings (WEs) similarity based search tool, considering the medical literature as case study. Using the compositional properties of the WEs we defined a methodology to aggregate the information coming from each word to obtain a vector corresponding to the abstracts of each PubMed article. Through this paradigm it is possible to capture the semantic content of the papers and, consequently, to evaluate and rank the similarity among them. The preliminary results with the proposed approach are obtained analysing a subset of the whole the PubMed collection. The results correctness has been verified by human domain experts, showing that the methodology is promising.
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Gargiulo, F., Silvestri, S., Fontanella, M., Ciampi, M., De Pietro, G. (2018). A Deep Learning Approach for Scientific Paper Semantic Ranking. In: De Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2017. KES-IIMSS-18 2018. Smart Innovation, Systems and Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-319-59480-4_47
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DOI: https://doi.org/10.1007/978-3-319-59480-4_47
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