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Eric Ringger

5 paper cards in Mova's library · 2002–2014

Eric Ringger wrote 5 of the papers Mova reads, in 2002–2014, most of them on Machine translation; often with Kevin Seppi, Deryle Lonsdale, Kristian Heal.

OpenAlex · h-index 21 · 1822 citations · 103 works — openalex, 2026-10-18

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology · ORCID

Topics: Machine translation 1

Papers

  1. Evaluating Lemmatization Models for Machine-Assisted Corpus-Dictionary Linkage (2014)
    The task is to link each word token of a Syriac corpus to the right dictionary headword (corpus-dictionary linkage), here reduced to stem-to-baseform linkage, since segmentation already reaches about 98.9% accuracy.
  2. First Results in a Study Evaluating Pre-annotation and Correction Propagation for Machine-Assisted Syriac Morphological Analysis (2012)
    The authors ask when machine pre-annotation (the annotator corrects a machine guess instead of starting from nothing) and correction propagation (a correction re-runs the model on the rest of the same sentence) make Syriac morphological annotation faster and more accurate.
  3. A Probabilistic Morphological Analyzer for Syriac (2010)
    The authors build a data-driven morphological analyzer for Syriac, an unvocalized Aramaic dialect with no existing tools, which needs only labelled data.
  4. CCASH: A Web Application Framework for Efficient, Distributed Language Resource Development (2010)
    CCASH is a web application framework for corpus annotation, built so that cost-efficient methods such as active learning and automatic pre-labelling can first be tried in user studies and then run on large projects.
  5. Machine-learned contexts for linguistic operations in German sentence realization (2002)
    The Amalgam realiser turns a semantic graph into a German surface tree in eight stages. Linguistic operations are written by hand, but the contexts in which each applies are learned as decision-tree classifiers, trained on 100,000 analysed sentences of technical manuals (70/30 split for training and tuning, then a…

Co-authors (14)

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