Discussions on Both Models
	• Capture the same information – The importance of a token’s position in the context of the search term – The sequential order of tokens • Different in complexity – Bigram model • Simplified Markov model with each token as a state • Captures token sequential information by bigram probabilities – PHMM model • More complex – aggregated token sequential information by hidden state transition probabilities • Experimental results show – PHMM is less sensitive to model length – PHMM may benefit more by using more training data