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The Bag of Words (BoW) approach represents text data as a collection of words, disregarding grammar and word order but maintaining word frequency.
Pragmatic ambiguity refers to situations where the meaning of a sentence depends on the context or pragmatic factors rather than the literal interpretation of the words alone.
In NLP, BoW approach can’t capture pragmatic ambiguity as it ignores the context and only focuses on individual words. This limitation can affect tasks like sentiment analysis or machine translation, where context plays a crucial role in determining the meaning of words or phrases. To address this, more advanced techniques like word embeddings or contextual models are used.