Machine Learning/Text Analysis/Algorithm Pointers
Yes, This absolutely makes sense. This is a common NLP (Natural Language Processing) problem. You should use word embedding models alingside LSTM (Long Short Term Memory) and deep neural networks. Actually, first you should represent every word as a vector of fixed dimension (e.g. 100) using word2vec. Then you should build a deep neural network architecture. The inputs of this network are your word vectors for each bill that are concatenated to form a sequence of vectors. The output of this network is a label that indicate which type of bill you are considering. I highly recommend that you use Keras which is a great python package for dealing with deep learning and NLP. Also, you can use existing word2vec sets that contain vector representations of most of words of a language (e.g. English). For example you can use GoogleNews word2vec set.