text: We present in this paper a novel framework for morpheme segmentation which uses the morpho-syntactic regularities preserved by word representations, in addition to orthographic features, to segment words into morphemes. This framework is the first to consider vocabulary-wide syntactico-semantic information for this task. We also analyze the deficiencies of available benchmarking datasets and introduce our own dataset that was created on the basis of compositionality. We validate our algorithm across datasets and present state-of-the-art results.
  @cite_32: We present a language agnostic, unsupervised method for inducing morphological transformations between words. The method relies on certain regularities manifest in highdimensional vector spaces. We show that this method is capable of discovering a wide range of morphological rules, which in turn are used to build morphological analyzers. We evaluate this method across six different languages and nine datasets, and show significant improvements across all languages.
summary: MORSE introduces semantic information into its morpheme segmenters via distributed word representations while also relying on orthographic features. Inspired by the work of @cite_32 , instead of merely evaluating semantic relatedness, we are the first to evaluate the morphological relationship via the difference vector of morphologically related words. Comparing the difference vectors of multiple pairs across the corpus following the same morphological relation, gives MORSE a vocabulary-wide evaluation of morphological relations learned.