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Hierarchical Word Mover Distance for Collaboration Recommender System

  • Chao SunEmail author
  • King Tao Jason Ng
  • Philip Henville
  • Roman Marchant
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 996)

Abstract

Natural Language Processing (NLP) techniques have enabled automated analysis over a large collection of documents, which makes it possible to quantitatively compare researcher profiles based on their publications. This paper proposes a novel researcher similarity measuring system which combines a variety of techniques, including topic modelling, Word2vec and word mover distance calculations on publication abstracts. The proposed method, implemented in python, matches researchers based upon a document’s texts by evaluating the semantic meanings of words and topics. The distances between researchers are calculated over various text features in an hierarchical structure. Results show that the system is successful in identifying existing co-authorships from sample data despite co-authorship properties having been removed, as well as suggesting valid potential academic collaboration links from related research areas irrespective of previous collaboration activity.

Notes

Acknowledgements

Dr Joel Nothman from the Sydney Informatics Hub has provided valuable suggestions and feedbacks to this work.

Prof. Nick Enfield, director of SSSHARC, Faculty of Arts and Social Sciences, the University of Sydney, initiated the question and supported this work.

The major development was conducted under the Capstone student project program initiated by the School of IT, the University of Sydney.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Faculty of Arts and Social SciencesThe University of SydneySydneyAustralia
  2. 2.Centre for Translational Data ScienceThe University of SydneySydneyAustralia
  3. 3.Faculty of Engineering and Information TechnologiesThe University of SydneySydneyAustralia

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