| Summary: | This project delves deep into the intricate nature of sarcasm detection in Natural Language Processing (NLP). Sarcasm, being a complex linguistic construct, often relies on contextual cues and incongruities, making its automatic detection particularly challenging in textual data. This research aims to identify and
dissect the multifaceted challenges posed by sarcasm in computational models. The study will analyse the performance of DL models in sarcasm prediction, examine the role of context, the effect of additional knowledge, and possibly explore potential strategies to overcome identified challenges.
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