Most work in statistical parsing has focused on a single corpus: the Wall Street Journal portion of the Penn Treebank. While this has allowed for quantitative comparison of parsing techniques, it has left open the question of how other types of text might a#ect parser performance, and how portable parsing models are across corpora. We examine these questions by comparing results for the Brown and WSJ corpora, and also consider which parts of the parser's probability model are particularly tuned to the corpus on which it was trained. This leads us to a technique for pruning parameters to reduce the size of the parsing model. 1

Over the years, many proposals have been made to incorporate assorted types of feature in language models. However, discrepancies between training sets, evaluation criteria, algorithms, and hardware environments make it difficult to compare the models objectively. In this paper, we take an information theoretic approach to select feature types in a systematic manner. We describe a quantitative analysis of the information gain and the information redundancy for various combinations of feature types inspired by both dependency structure and bigram structure, using a Chinese treebank and taking word prediction as the object. The experiments yield several conclusions on the predictive value of several feature types and feature types combinations for word prediction, which are expected to provide guidelines for feature type selection in language modeling.

In this paper, a distance-related unit association maximum entropy (DUAME) language modeling is proposed. This approach can model an event (unit subsequence) using the co-occurrence of full distance unit association (UA) features so that it is able to pursue a functional approximation to higher order N-gram with significantly less memory requirement. A smoothing strategy related to this modeling will also be discussed. Preliminary experimental results have shown that DUAME modeling is comparable to conventional N-gram modeling in perplexity with significantly small number of parameters.