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Enriching, Editing, and Representing Interlinear Glossed Text

  • Fei Xia
  • Michael Wayne Goodman
  • Ryan Georgi
  • Glenn Slayden
  • William D. Lewis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9041)

Abstract

The majority of the world’s languages have little to no NLP resources or tools. This is due to a lack of training data (“resources”) over which tools, such as taggers or parsers, can be trained. In recent years, there have been increasing efforts to apply NLP methods to a much broader swathe of the worlds languages. In many cases this involves bootstrapping the learning process with enriched or partially enriched resources. One promising line of research involves the use of Interlinear Glossed Text (IGT), a very common form of annotated data used in the field of linguistics. Although IGT is generally very richly annotated, and can be enriched even further (e.g., through structural projection), much of the content is not easily consumable by machines since it remains “trapped” in linguistic scholarly documents and in human readable form. In this paper, we introduce several tools that make IGT more accessible and consumable by NLP researchers.

Keywords

Syntactic Structure Computational Linguistics Word Alignment Translation Line Human Readable Form 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Fei Xia
    • 1
  • Michael Wayne Goodman
    • 1
  • Ryan Georgi
    • 1
  • Glenn Slayden
    • 1
  • William D. Lewis
    • 2
  1. 1.Linguistics DepartmentUniversity of WashingtonSeattleUSA
  2. 2.Microsoft ResearchRedmondUSA

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