Final answer:
The advantage of using a bigram language model for ranking is improved accuracy, as it considers pairs of words. However, it requires more computational resources and can be slower than unigram models.
Step-by-step explanation:
The advantage of using a bigram language model for ranking instead of unigram is that it can capture more context and improve accuracy. Bigram models consider pairs of words, which can help to better understand the meaning and intention behind a sequence of words. For example, in the sentence 'I love cats,' a bigram model would recognize that the word 'love' is a verb and not a noun. This can lead to more accurate rankings.
On the other hand, a disadvantage of using bigram models is that they require more computational resources and time compared to unigram models. The larger vocabulary and increased complexity of analyzing pairs of words can slow down the prediction process. Additionally, bigram models may struggle with out-of-vocabulary words or rare combinations that were not encountered during training.