Pointwise Prediction for Robust, Adaptable Japanese Morphological Analysis

要旨

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. 1

主題