Paper: The Role Of Algorithm Bias Vs. Information Source In Learning Algorithms For Morphosyntactic Disambiguation

ACL ID W00-0704
Title The Role Of Algorithm Bias Vs. Information Source In Learning Algorithms For Morphosyntactic Disambiguation
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2000
Authors

Morphosyntactic Disambiguation (Part of Speech tagging) is a useful benchmark problem for system comparison because it is typical for a large class of Natural Language Process- ing (NLP) problems that can be defined as disambiguation in local context. This paper adds to the literature on the systematic and objective evaluation of different methods to automatically learn this type of disambiguation problem. We systematically compare two inductive learning approaches to tagging: MX- POST (based on maximum entropy modeling) and MBT (based on memory-based learning). We investigate the effect of different sources of information on accuracy when comparing the two approaches under the same conditions. Results indicate that earlier observed differ- ences in accuracy can be attributed largely to di...