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Word Embeddings for Unsupervised Named Entity Linking

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Book cover Knowledge Science, Engineering and Management (KSEM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11776))

Abstract

The huge amount of textual user-generated content on the Web has incredibly grown in the last decade, creating new relevant opportunities for different real-world applications and domains. In particular, microblogging platforms enables the collection of continuously and instantly updated information. The organization and extraction of valuable knowledge from these contents are fundamental for ensuring profitability and efficiency to companies and institutions. This paper presents an unsupervised model for the task of Named Entity Linking in microblogging environments. The aim is to link the named entity mentions in a text with their corresponding knowledge-base entries exploiting a novel heterogeneous representation space characterized by more meaningful similarity measures between words and named entities, obtained by Word Embeddings. The proposed model has been evaluated on different benchmark datasets proposed for Named Entity Linking challenges for English and Italian language. It obtains very promising performance given the highly challenging environment of user-generated content over microblogging platforms.

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Notes

  1. 1.

    In the experimental investigation, the considered NER model is the one proposed by Ritter et al. [33], which has been specifically designed for dealing with user-generated content.

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Acknowledgements

This work has been partially supported by PON I&R 2014-20, with the grant for research project “SmartCal”, CUP B48I15000180008.

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Authors and Affiliations

  1. University of Milano - Bicocca, Milan, Italy

    Debora Nozza, Cezar Sas, Elisabetta Fersini & Enza Messina

Authors
  1. Debora Nozza
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  2. Cezar Sas
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  3. Elisabetta Fersini
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  4. Enza Messina
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Corresponding author

Correspondence to Debora Nozza .

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Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Christos Douligeris

  2. University of Vienna, Vienna, Austria

    Dimitris Karagiannis

  3. University of Piraeus, Piraeus, Greece

    Dimitris Apostolou

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Nozza, D., Sas, C., Fersini, E., Messina, E. (2019). Word Embeddings for Unsupervised Named Entity Linking. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11776. Springer, Cham. https://doi.org/10.1007/978-3-030-29563-9_13

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  • DOI: https://doi.org/10.1007/978-3-030-29563-9_13

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