Developing Machine Learning Models That Understand Context and Nuance in Online Language

EasyChair Preprint 13040

Abstract

The advent of machine learning has revolutionized the analysis of online language, yet challenges remain in developing models that truly understand context and nuance. This paper explores recent advancements and methodologies in developing machine learning models that can better comprehend the subtleties of online language. We discuss the importance of context in interpreting language and review techniques such as contextual embeddings, attention mechanisms, and transformer models that have significantly improved contextual understanding. Additionally, we examine the role of annotated datasets and transfer learning in training these models effectively. Finally, we discuss future directions, including the integration of multimodal inputs and the development of models that can adapt to evolving online language trends.

Keyphrases: AI, machine learning, online language

@booklet{EasyChair:13040,
  author    = {Abil Robert},
  title     = {Developing Machine Learning Models That Understand Context and Nuance in Online Language},
  howpublished = {EasyChair Preprint 13040},
  year      = {EasyChair, 2024}}