Abstract
Deduplication is the task of identifying near and exact duplicate data items in a collection. In this paper, we present a novel method for deduplication of scholarly documents. We develop a hybrid model which uses structural similarity (locality sensitive hashing) and meaning representation (word embeddings) of document texts to determine (near) duplicates. Our collection constitutes a subset of multidisciplinary scholarly documents aggregated from research repositories. We identify several issues causing data inaccuracies in such collections and motivate the need for deduplication. In lack of existing dataset suitable for study of deduplication of scholarly documents, we create a ground truth dataset of 100K scholarly documents and conduct a series of experiments to empirically establish optimal values for the parameters of our deduplication method. Experimental evaluation shows that our method achieves a macro F1-score of 0.90. We productionise our method as a publicly accessible web API service serving deduplication of scholarly documents in real time.- Anthology ID:
- 2020.lrec-1.113
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 901–910
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.113
- DOI:
- Cite (ACL):
- Bikash Gyawali, Lucas Anastasiou, and Petr Knoth. 2020. Deduplication of Scholarly Documents using Locality Sensitive Hashing and Word Embeddings. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 901–910, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Deduplication of Scholarly Documents using Locality Sensitive Hashing and Word Embeddings (Gyawali et al., LREC 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.lrec-1.113.pdf