Paper: Adaptive Transformation-Based Learning For Improving Dictionary Tagging

ACL ID E06-1033
Title Adaptive Transformation-Based Learning For Improving Dictionary Tagging
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

We present an adaptive technique that en- ables users to produce a high quality dic- tionary parsed into its lexicographic com- ponents (headwords, pronunciations, parts of speech, translations, etc). using an extremely small amount of user provided training data. We use transformation- based learning (TBL) as a postprocessor at two points in our system to improve per- formance. The results using two dictio- naries show that the tagging accuracy in- creases from 83% and 91% to 93% and 94% for individual words or “tokens”, and from 64% and 83% to 90% and 93% for contiguous “phrases” such as definitions or examples of usage.