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DigitalAngewandte InformatikAngewandte Wirtschaftswissenschaften
Extracting Keywords from Publication Abstracts for an Automated Researcher Recommendation System
Digitale Welt, vol. 4, no. 1, pp. 20-25
This paper presents an automated keyword assignment system for scientific abstracts. That system is applied to paper abstracts collected in a local publication database and used to drive a researcher recommendation system. Problems like low data volume and missing keywords are discussed. For remediation, training is performed on an extended data set based on large online publication databases. Additionally a closer look at label imbalance in the dataset is taken. Ten multi-label classification algorithms for assigning keywords from a given catalogue to a scientific abstract are compared. The usage of binary relevance as transformation method with LightGBM as classifier yields the best results. Random oversampling before the training phase additionally increases the F1-Score by around 5-6%.