Autores
Aroyehun Segun Taofeek
Angel Gil Jason Efrain
Majumder Navonil
Gelbukh Alexander
Título Leveraging label hierarchy using transfer and multi-task learning: A case study on patent classification
Tipo Revista
Sub-tipo JCR
Descripción Neurocomputing
Resumen When labels are organized into a meaningful taxonomy, the parent-child relationship between labels at different levels can give the classifier additional information not deducible from the data alone, especially with limited training data. As a case study, we illustrate this effect on the task of patent classification-the task of categorizing patent documents based on their technical content. Existing approaches do not take into consideration this additional information. Experiments on two patent classification datasets, WIPOalpha and USPTO-2M, show that our regularized Gated Recurrent Unit (GRU) architecture already gives a performance improvement with a micro-averaged precision score using the top prediction of 0.5191 and 0.5740 on the two datasets, respectively. However, knowledge transfer along the label hierarchy gives further significant improvement on WIPO-alpha, raising the score to 0.5376, and a small improvement on USPTO-2M to 0.5743. Our analyses reveal that incorporating label information improves performance on classes with fewer examples and makes model robust to errors that result from predicting closely related labels. (c) 2021 Elsevier B.V. All rights reserved.
Observaciones DOI 10.1016/j.neucom.2021.07.057
Lugar Amsterdam
País Paises Bajos
No. de páginas 421-431
Vol. / Cap. v. 464
Inicio 2021-11-13
Fin
ISBN/ISSN