Investigating linguistic knowledge in a maximum entropy token-based language model

We present a novel language model capable of incorporating various types of linguistic information as encoded in the form of a token, a (word, label)-tuple. Using tokens as hidden states, our model is effectively a hidden Markov model (HMM) producing sequences of words with trivial output distributions. The transition probabilities, however, are computed using a maximum entropy model to take advantage of potentially overlapping features. We investigated different types of labels with a wide range of linguistic implications. These models outperform Kneser-Ney smoothed n-gram models both in terms of perplexity on standard datasets and in terms of word error rate for a large vocabulary speech recognition system.