Neural Morphological Analysis: Encoding-Decoding Canonical Segments
Katharina Kann, Ryan Cotterell, Hinrich Schütze · 2016
Canonical morphological segmentation aims to divide words into a sequence of standardized segments.In this work, we propose a character-based neural encoderdecoder model for this task.Additionally, we extend our model to include morphemelevel and lexical information through a neural reranker.We set the new state of the art for the task improving previous results by up to 21% accuracy.Our experiments cover three languages: English, German and Indonesian.RR ED Joint WFST UB error en .19(.01) .25 (.01) 0.27 (.02) 0.63 (.01) .06(.01) de .20 (.01) .26(.02) 0.41 (.03) 0.74 (.01) .04(.01) id .05(.01) .09(.01) 0.10 (.01) 0.71 (.01) .02(.01) edit en .21(.02) .47(.02) 0.98 (.34) 1.35 (.01) .10(.02) de .29 (.02) .51(.03) 1.01 (.07) 4.24 (.20) .06(.01) id .05(.00) .12(.01) 0.15 (.02) 2.13 (.01) .02(.01) F1