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Abstract for niesler_tr258

Cambridge University Engineering Department Technical Report CUED/F-INFENG/TR258


Thomas Niesler and Phil Woodland

May 1996

A language model combining word-based and category-based \ngrams within a backoff framework is presented. Word \ngrams conveniently capture sequential relations between particular words, while the category-model, which is based on part-of-speech classifications and allows ambiguous category membership, is able to generalise to unseen word sequences and therefore appropriate in backoff situations. Experiments on the LOB, Switchboard and WSJ0 corpora demonstrate that the technique greatly improves language model perplexities for sparse training sets, and offers significantly improved complexity versus performance tradeoffs when compared with standard trigram models.

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