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Authorship recognition and disambiguation of scientific papers using a neural networks approach
- Source :
- Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD).
- Publication Year :
- 2018
- Publisher :
- Sissa Medialab, 2018.
-
Abstract
- Authorship recognition and author names disambiguation are main issues affecting the quality and reliability of bibliographic records retrieved from digital libraries, such as Web of Science, Scopus, Google Scholar and many others. So far, these problems have been faced using methods mainly based on text-pattern-recognition for specific datasets, with high-level degree of errors. In this paper, we propose a different approach using neural networks to learn features automatically for solving authorship recognition and disambiguation of author names. The network learns for each author the set of co-writers, and from this information recovers authorship of papers. In addition, the network can be trained taking into account other features, such as author affiliations, keywords, projects and research areas. The network has been developed using the TensorFlow framework, and run on recent Nvidia GPUs and multi-core Intel CPUs. Test datasets have been selected from records of Scopus digital library, for several groups of authors working in the fields of computer science, environmental science and physics. The proposed methods achieves accuracies above 99\% in authorship recognition, and is able to effectively disambiguate homonyms. We have taken into account several network parameters, such as training-set and batch size, number of levels and hidden units, weights initialization, back-propagation algorithms, and analyzed also their impact on accuracy of results. This approach can be easily extended to any dataset and any bibliographic records provider.
- Subjects :
- Information retrieval
Neural Networks
Artificial neural network
Computer science
Research areas
media_common.quotation_subject
Reliability (computer networking)
Scopus
Initialization
PE1_18
Digital library
NO
Database
Set (abstract data type)
Neural Networks, Database
Quality (business)
media_common
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)
- Accession number :
- edsair.doi.dedup.....52d56d3e0a48c966d2e0f1c20c9e33fa