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Erschienen in:
Hand-Designed Local Image Descriptors vs. Off-the-Shelf CNN-Based Features for Texture Classification: An Experimental Comparison Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

A Deep Learning Approach for Scientific Paper Semantic Ranking

verfasst von : Francesco Gargiulo, Stefano Silvestri, Mariarosaria Fontanella, Mario Ciampi, Giuseppe De Pietro

Erschienen in: Intelligent Interactive Multimedia Systems and Services 2017

Verlag: Springer International Publishing

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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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Metadaten
Titel
A Deep Learning Approach for Scientific Paper Semantic Ranking
verfasst von
Francesco Gargiulo
Stefano Silvestri
Mariarosaria Fontanella
Mario Ciampi
Giuseppe De Pietro
Copyright-Jahr
2018
Buch
Intelligent Interactive Multimedia Systems and Services 2017

Print ISBN: 978-3-319-59479-8

Electronic ISBN: 978-3-319-59480-4

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-59480-4_47

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