How do the results change when bigrams are used with the bag-of-words compared to single words?

How do the results change when bigrams are used with the bag-of-words compared to single words?

• A. The results remain the same
• B. The results can be incorrectly calculated
• C. The results can be enormously different
• D. The results are always improved

Answer: (C)

Utilizing bigrams in addition to single words within a bag-of-words model can lead to significantly different outcomes because bigrams capture the sequential relationships between two adjacent words. This allows for a richer representation of context and phrase structure that single words alone cannot provide. For instance, the meaning of phrases can change entirely based on the combination of words in a bigram, which is not captured in a traditional bag-of-words approach where context is lost as each word is treated independently. In applications such as sentiment analysis, topic modeling, or any other NLP tasks, incorporating bigrams can improve the model's ability to detect nuances in language. As a result, performance metrics like accuracy, precision, and recall may vary considerably when comparing results generated with bigrams against those generated with single words. The other answers do not accurately reflect the complexity introduced by bigrams. Stating that results remain the same overlooks the potential for improved context understanding. Claiming results can be incorrectly calculated misrepresents the statistical integrity of the approaches; both techniques can yield valid results, but their implications differ significantly. Finally, indicating that results are always improved does not account for scenarios in which linearly increasing complexity could lead to overfitting, especially with limited

Utilizing bigrams in addition to single words within a bag-of-words model can lead to significantly different outcomes because bigrams capture the sequential relationships between two adjacent words. This allows for a richer representation of context and phrase structure that single words alone cannot provide. For instance, the meaning of phrases can change entirely based on the combination of words in a bigram, which is not captured in a traditional bag-of-words approach where context is lost as each word is treated independently.

In applications such as sentiment analysis, topic modeling, or any other NLP tasks, incorporating bigrams can improve the model's ability to detect nuances in language. As a result, performance metrics like accuracy, precision, and recall may vary considerably when comparing results generated with bigrams against those generated with single words.

The other answers do not accurately reflect the complexity introduced by bigrams. Stating that results remain the same overlooks the potential for improved context understanding. Claiming results can be incorrectly calculated misrepresents the statistical integrity of the approaches; both techniques can yield valid results, but their implications differ significantly. Finally, indicating that results are always improved does not account for scenarios in which linearly increasing complexity could lead to overfitting, especially with limited