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Semantically Aware Text Categorisation for Metadata Annotation

  • Giulio CarducciEmail author
  • Marco Leontino
  • Daniele P. Radicioni
  • Guido Bonino
  • Enrico Pasini
  • Paolo Tripodi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 988)

Abstract

In this paper we illustrate a system aimed at solving a long-standing and challenging problem: acquiring a classifier to automatically annotate bibliographic records by starting from a huge set of unbalanced and unlabelled data. We illustrate the main features of the dataset, the learning algorithm adopted, and how it was used to discriminate philosophical documents from documents of other disciplines. One strength of our approach lies in the novel combination of a standard learning approach with a semantic one: the results of the acquired classifier are improved by accessing a semantic network containing conceptual information. We illustrate the experimentation by describing the construction rationale of training and test set, we report and discuss the obtained results and conclude by drawing future work.

Keywords

Text categorization Lexical resources Semantics NLP Language models 

Notes

Acknowledgments

The authors wish to thank the EThOS staff for their prompt and kind support. Giulio Carducci and Marco Leontino have been supported by the REPOSUM project, BONG_CRT_17_01 funded by Fondazione CRT.

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Authors and Affiliations

  1. 1.Dipartimento di InformaticaUniversità degli Studi di TorinoTurinItaly
  2. 2.Dipartimento di FilosofiaUniversità degli Studi di TorinoTurinItaly

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