Paper: Pointwise Prediction for Robust Adaptable Japanese Morphological Analysis

ACL ID P11-2093
Title Pointwise Prediction for Robust Adaptable Japanese Morphological Analysis
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

We present a pointwise approach to Japanese morphological analysis (MA) that ignores structure information during learning and tag- ging. Despite the lack of structure, it is able to outperform the current state-of-the-art struc- tured approach for Japanese MA, and achieves accuracy similar to that of structured predic- tors using the same feature set. We also find that the method is both robust to out- of-domain data, and can be easily adapted through the use of a combination of partial an- notation and active learning.