Incorporation of WordNet Features to n-gram Features in a Language Modeler
Kathleen Go, Solomon See · Institutional Repositories DataBase (IRDB)
n-gram language modeling is a popular technique used to improve performance of various NLP applications. However, it still faces the “curse of dimensionality” issue wherein word sequences on which the model will be tested are likely to be different from those seen during training (Bengio et al., 2003). An approach that incorporates WordNet to a trigram language modeler has been developed to address this issue. WordNet was used to generate proxy trigrams that may be used to reinforce the fluency of the given trigrams. Evaluation results reported a significant decrease in model perplexity showing that the new method, evaluated using the English language in the business news domain, is capable of addressing the issue. The modeler was also used as a tool to rank parallel translations produced by multiple Machine Translation systems. Results showed a 6-7% improvement over the base approach (Callison-Burch and Flournoy, 2001) in correctly ranking parallel translations.