Gap Analysis of Natural Language Processing Systems with respect to Linguistic Modality
This work addresses a fundamental linguistic challenge for NLP systems, but it is incremental as it primarily reviews and highlights gaps without presenting new methods or results.
The paper identifies a gap in current NLP systems regarding their ability to handle the contextual nature of linguistic modality, which involves expressing attitudes or assessments, and reviews existing approaches while suggesting future directions.
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.