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_9: We present conditional random fields , a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those models. Conditional random fields also avoid a fundamental limitation of maximum entropy Markov models (MEMMs) and other discriminative Markov models based on directed graphical models, which can be biased towards states with few successor states. We present iterative parameter estimation algorithms for conditional random fields and compare the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
  @cite_6: We discuss data-driven morphological segmentation, in which word forms are segmented into morphs, that is the surface forms of morphemes. We extend a recent segmentation approach based on conditional random fields from purely supervised to semi-supervised learning by exploiting available unsupervised segmentation techniques. We integrate the unsupervised techniques into the conditional random field model via feature set augmentation. Experiments on three diverse languages show that this straightforward semi-supervised extension greatly improves the segmentation accuracy of the purely supervised CRFs in a computationally efficient manner.
  @cite_11: We discuss data-driven morphological segmentation, in which word forms are segmented into morphs, the surface forms of morphemes. Our focus is on a lowresource learning setting, in which only a small amount of annotated word forms are available for model training, while unannotated word forms are available in abundance. The current state-of-art methods 1) exploit both the annotated and unannotated data in a semi-supervised manner, and 2) learn morph lexicons and subsequently uncover segmentations by generating the most likely morph sequences. In contrast, we discuss 1) employing only the annotated data in a supervised manner, while entirely ignoring the unannotated data, and 2) directly learning to predict morph boundaries given their local sub-string contexts instead of learning the morph lexicons. Specifically, we employ conditional random fields, a popular discriminative log-linear model for segmentation. We present experiments on two data sets comprising five diverse languages. We show that the fully supervised boundary prediction approach outperforms the state-of-art semi-supervised morph lexicon approaches on all languages when using the same annotated data sets.
summary: @cite_9 use Conditional Random Fields (CRF) for morpheme segmentation. In this supervised method, the morpheme segmentation task is modeled as a sequence-to-sequence learning problem, whereby the sequence of labels defines the boundaries of morphemes @cite_11 @cite_6 . In contrast to the previously mentioned generative approaches of MDL and AG, this method takes a discriminative approach and allows for the inclusion of a larger set of features. In this approach, CRF learns a conditional probability of a segmentation given a word @cite_11 @cite_6 .