Structure and Performance of a Dependency Language Model
- Ciprian Chelba ,
- David Engle ,
- Frederick Jelinek ,
- Victor Jimenez ,
- Sanjeev Khudanpur ,
- Lidia Mangu ,
- Harry Printz ,
- Eric Ristad ,
- Ronald Rosenfeld ,
- Andreas Stolcke ,
- Dekai Wu
Proceedings of the Eurospeech Conference |
We present a maximum entropy language model that incorporates both syntax and semantics via a dependency grammar. Such a grammar expresses the relations between words by a directed graph. Because the edges of this graph may connect words that are arbitrarily far apart in a sentence, this technique can incorporate the predictive power of words that lie outside of bigram or trigram range. We have built several simple dependency models, as we call them, and tested them in a speech recognition experiment. We report experimental results for these models here, including one that has a small but statistically signi cant advantage (p < :02) over a bigram language model.