Machine Learning Approach for Ambiguity Detection in Social Media Context

In the field of Natural Language Processing (NLP), understanding the nuances of pragmatic ambiguities, where words or phrases can have multiple meanings based on context, remains a challenge. Beyond traditional NLP tasks, detecting pragmatic ambiguities can enhance human-robot interactions, improve virtual assistants, and aid in interpreting complex documents like legal contracts. This presents the application of artificial Intelligence (AI) or machine learning to detect such ambiguities. This paper introduces a novel framework for the detection of pragmatic ambiguities, particularly focusing on sarcasm, irony, and contextual ambiguity. Employing pre-processed data, the framework utilizes a BERT model and a Bidirectional LSTM (Bi-LSTM) network for the respective tasks. These models operate in a multi-task learning environment, which serves to enhance both learning efficiency and predictive accuracy. Comparative analysis with existing methods such as SVM and LSTM reveals the superior performance of our proposed framework, achieving an accuracy rate of
$98.00{{\% }}$
. The findings validate the efficacy of our approach, suggesting its applicability in the development of more robust natural language processing systems.