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Document Aboutness via Sophisticated Syntactic and Semantic Features

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Natural Language Processing and Information Systems (NLDB 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10260))

Abstract

The document aboutness problem asks for creating a succinct representation of a document’s subject matter via keywords, sentences or entities drawn from a Knowledge Base. In this paper we propose an approach to solve this problem which improves the known solutions over all known datasets [4, 19]. It is based on a wide and detailed experimental study of syntactic and semantic features drawn from the input document thanks to the use of some IR/NLP tools. To encourage and support reproducible experimental results on this task, we will make accessible our system via a public API: this is the first, and best performing, tool publicly available for the document aboutness problem.

This work has been supported in part by the EU H2020 Program under the scheme “INFRAIA-1-2014-2015: Research Infrastructures” grant agreement #654024 “SoBigData: Social Mining & Big Data Ecosystem”.

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Notes

  1. 1.

    pages.di.unipi.it/ponza/research/aboutness.

  2. 2.

    Thanks to the authors of [4] for pointing this out to us.

  3. 3.

    Remind that Sel’s implementation is not available.

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Author information

Authors and Affiliations

  1. Dipartimento di Informatica, University of Pisa, Pisa, Italy

    Marco Ponza, Paolo Ferragina & Francesco Piccinno

Authors
  1. Marco Ponza
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  2. Paolo Ferragina
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  3. Francesco Piccinno
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Corresponding author

Correspondence to Marco Ponza .

Editor information

Editors and Affiliations

  1. Erasmus University Rotterdam, Rotterdam, The Netherlands

    Flavius Frasincar

  2. University of Liège , Liège, Belgium

    Ashwin Ittoo

  3. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Le Minh Nguyen

  4. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

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Ponza, M., Ferragina, P., Piccinno, F. (2017). Document Aboutness via Sophisticated Syntactic and Semantic Features. In: Frasincar, F., Ittoo, A., Nguyen, L., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science(), vol 10260. Springer, Cham. https://doi.org/10.1007/978-3-319-59569-6_53

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  • DOI: https://doi.org/10.1007/978-3-319-59569-6_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59568-9

  • Online ISBN: 978-3-319-59569-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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