Given the task definition and input, reply with output. In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

The involved algorithms are detailed as follows.

CNN-C denotes the CNN with (Chinese) character embedding.

CNN-W denotes the CNN with (Chinese) word embedding.

CNN-Lex-C denotes the algorithm which also integrates polar words in CNN which is proposed by Shin et al. BIBREF24 . The (Chinese) character embedding is used.

CNN-Lex-W denotes the algorithm which also integrates polar words in CNN which is proposed by Shin et al. BIBREF24 . The (Chinese) word embedding is used.

Bi-LSTM-C denotes the BI-LSTM with (Chinese) character embedding.

Bi-LSTM-W denotes the Bi-LSTM with (Chinese) word embedding.

Lex-rule denotes the rule-based approach shows in Fig. 1. This approach is unsupervised.

BOW denotes the conventional algorithm which is based of bag-of-words features. 
 Question: What are the other models they compare to?

CNN-C CNN-W CNN-Lex-C CNN-Lex-W Bi-LSTM-C  Bi-LSTM-W Lex-rule BOW