Gap Analysis of Natural Language Processing Systems with respect to Linguistic Modality

TL;DR

This paper reviews the limitations of current NLP systems in understanding linguistic modality, emphasizing the importance of context and proposing future research directions for more cognitively aligned models.

Contribution

It provides a comprehensive analysis of the gap in NLP systems' handling of linguistic modality and discusses future research avenues for improving contextual understanding.

Findings

Current NLP systems lack deep understanding of linguistic modality.

Contextual nature of modality poses challenges for NLP.

Future research should focus on cognitive and multi-layered approaches.

Abstract

Modality is one of the important components of grammar in linguistics. It lets speaker to express attitude towards, or give assessment or potentiality of state of affairs. It implies different senses and thus has different perceptions as per the context. This paper presents an account showing the gap in the functionality of the current state of art Natural Language Processing (NLP) systems. The contextual nature of linguistic modality is studied. In this paper, the works and logical approaches employed by Natural Language Processing systems dealing with modality are reviewed. It sees human cognition and intelligence as multi-layered approach that can be implemented by intelligent systems for learning. Lastly, current flow of research going on within this field is talked providing futurology.