Paper: Detection Of Language (Model) Errors

ACL ID W00-1311
Title Detection Of Language (Model) Errors
Venue 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora
Session Main Conference
Year 2000
Authors

The bigram language models are popular, in much language processing applications, in both Indo-European and Asian languages. However, when the language model for Chinese is applied in a novel domain, the accuracy is reduced significantly, from 96% to 78% in our evaluation. We apply pattern recognition techniques (i.e. Bayesian, decision tree and neural network classifiers) to discover language model errors. We have examined 2 general types of features: model- based and language-specific features. In our evaluation, Bayesian classifiers produce the best recall performance of 80% but the precision is low (60%). Neural network produced good recall (75%) and precision (80%) but both Bayesian and Neural network have low skip ratio (65%). The decision tree classifier produced the best precision ...