Using Transfer Learning to Assist Exploratory Corpus Annotation
Paul Felt, Eric K. Ringger, Kevin D. Seppi, Kristian S. Heal · 2014
We describe an under-studied problem in language resource management: that of providing automatic assistance to annotators working in exploratory settings.When no satisfactory tagset already exists, such as in under-resourced or undocumented languages, it must be developed iteratively while annotating data.This process naturally gives rise to a sequence of datasets, each annotated differently.We argue that this problem is best regarded as a transfer learning problem with multiple source tasks.Using part-of-speech tagging data with simulated exploratory tagsets, we demonstrate that even simple transfer learning techniques can significantly improve the quality of pre-annotations in an exploratory annotation.