|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
A VARIABLE-LENGTH CATEGORY-BASED N-GRAM LANGUAGE MODEL
Thomas Niesler and Phil Woodland
A language model based on word-category n-grams and ambiguous category membership with n increased selectively to trade compactness for performance is presented. The use of categories leads intrinsically to a compact model with the ability to generalise to unseen word sequences, and diminishes the spareseness of the training data, thereby making larger n feasible. The language model implicitly involves a statistical tagging operation, which may be used explicitly to assign category assigments to untagged text. Experiments on the LOB corpus show the optimal model-building strategy to yield improved results with respect to conventional n-gram methods, and when used as a tagger, the model is seen to perform well in relation to a standard benchmark.
If you have difficulty viewing files that end
which are gzip compressed, then you may be able to find
tools to uncompress them at the gzip
If you have difficulty viewing files that are in PostScript, (ending
'.ps.gz'), then you may be able to
find tools to view them at
We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.
|| Search | CUED | Cambridge University ||
2005 Cambridge University Engineering Dept
Information provided by milab-maintainer