Publikation
DeepKAF: A Heterogeneous CBR Deep Learning Approach for NLP Prototyping
Kareem Amin; Stelios Kapetanakis; Nikolaos Polatidis; Klaus-Dieter Althoff; Andreas Dengel
In: IEEE (Hrsg.). 2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA). INISTA (INISTA-2020), located at INnovations in Intelligent SysTems and Applications, August 24-26, Novi Sad, Serbia, Pages 1-7, No. 19951570, IEEE, New York, 9/2020.
Zusammenfassung
With widespread modernization, digitization and transformations of most of industries, Artificial Intelligence (AI) has become the key enabler in that modernization journey. AI offers substantial capabilities to solve new problems and optimise existing solutions specialising on specific problems and learning from different domains. AI solutions can be either explainable or black box ones with the latter being urged to improve since they cannot trust. Case-based Reasoning (CBR) is an explainable AI approach where solutions are provided along with relevant explanations in terms of why a solution was selected. However, CBR, like most other explainable approaches, has several limitations in terms of scalability, large data volumes, domain complexity, that reduce its ability to scale any CBR system in industrial applications. In this paper, we provide a heterogeneous CBR framework - DeepKAF where we combine CBR paradigm with Deep Learning architectures to solve complicated Natural Language Processing (NLP) problems (eg. mixed language and grammatically incorrect text).DeepKAF is built based on continuous research in the area of Deep Learning and CBR. DeepKAF has been implemented and used across different domains, test use cases and research models as an ensemble deep learning and CBR Architecture.