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Key Challenges in Natural Language Processing Explained

Semantics and Pragmatic Understanding: It is a huge challenge to understand the semantic and pragmatic meaning of a language. Many times, the meaning of words and their pragmatic messages may differ, making it difficult for models to understand what a phrase means.

Language Diversity: The diversity of styles, dramatics and punctuation in language is another challenge. Proper understanding of the same word or sentence from different contexts can be problematic.

Challenge of Translation: Translating languages is yet another uphill task due to the need for linguisticism, semantic descriptions and coherence in language.

Availability of Data: Also, lack of big data with high quality is a challenge since practicing with good and balanced dataset can help improve models.

Deep Learning: Large volume of data is necessary for more profound education so that models would succeed even in fine-grained analysis of language.

Social pragmatics: Natural Language Processing models find it hard to comprehend social pragmatics, which make them incapable to correctly empathize with users’ feelings.