DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NLP Prototyping

Kareem Amin, Stelios Kapetanakis, Nikolaos Polatidis, Klaus-Dieter Althoff, Andreas Dengel

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationINISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings
Subtitle of host publicationINISTA 2020
EditorsMirjana Ivanovic, Tulay Yildirim, Goce Trajcevski, Costin Badica, Ladjel Bellatreche, Igor Kotenko, Amelia Badica, Burcu Erkmen, Milos Savic
PublisherIEEE
Pages1-7
ISBN (Electronic)9781728167992
ISBN (Print)9781728168005
DOIs
Publication statusPublished - 11 Sep 2020
EventIEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) -
Duration: 24 Aug 202026 Aug 2020

Publication series

NameINISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings

Conference

ConferenceIEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)
Period24/08/2026/08/20

Keywords

  • Case-based Reasoning
  • Deep Learning
  • Natural Language Processing

Fingerprint

Dive into the research topics of 'DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NLP Prototyping'. Together they form a unique fingerprint.

Cite this