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.
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Notes
- 1.
Downloaded from https://github.com/idio/wiki2vec/.
- 2.
Check https://dev.elsevier.com/sc_apis.html for more details.
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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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Sun, C., Ng, K.T.J., Henville, P., Marchant, R. (2019). Hierarchical Word Mover Distance for Collaboration Recommender System. In: Islam, R., et al. Data Mining. AusDM 2018. Communications in Computer and Information Science, vol 996. Springer, Singapore. https://doi.org/10.1007/978-981-13-6661-1_23
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