Deep Learning for Natural Language Processing

https://sites.google.com/view/dlnlp2024-at-aiccsa/home

ORGANIZING COMMITTEE:

  • Lamia Hadrich Belguith (ANLP Research Group, MIRACL Lab., FSEG, University of Sfax,Tunisia)
  • Maher Jaoua (ANLP Research Group, MIRACL Lab., FSEG, University Of Sfax, Tunisia)
  • Samira Ellouze (ANLP Research Group, MIRACL Lab., ISIMG, University of Gabes,Tunisia)

Overview:

Natural Language Processing (NLP) presents a significant challenge within computational linguistics due to the intricate grammatical structures, intricate morphologies, and orthographic ambiguities found in numerous languages. These diverse linguistic characteristics amplify the complexity of NLP tasks, necessitating the development of robust methodologies. We need to think about innovative methods that can deal with the linguistic complexity.
Deep learning techniques have demonstrated remarkable performance across a wide array of NLP tasks. Deep Learning for Natural Language Processing workshop aims to explore the recent advancements in deep learning architectures and techniques that can be used to resolve the complexity of language phenomena encountered in various NLP applications such as machine translation, sentiment analysis, chatbots, and more.
This workshop serves as a platform for researchers and practitioners actively involved in the development of NLP applications. Participants will have the opportunity to present and discuss the recent fundamental and applied research works, providing novel solutions to emerging challenges in different NLP fields.
Topics The Deep Learning for Natural Language Processing workshop will explore a wide range of applications and techniques, including but not limited to:

  • Pre-training or fine-tuning large language models,
  • Information extraction,
  • Sentiment analysis,
  • Named entity recognition and disambiguation,
  • Text classification, text generation, text summarization and text simplification,
  • Social media analytics,
  • Optical character recognition,
  • Speech synthesis,
  • Machine translation,
  • Pattern recognition,
  • Syntactic analysis,
  • Part-of-speech tagging,
  • Dialect identification,
  • Dialect translation,
  • Fake news detection,
  • Financial NLP,
  • Healthcare NLP,
  • Plagiarism detection,
  • Authorship identification and verification