Language Resources and Evaluation

, Volume 50, Issue 2, pp 321–349 | Cite as

Enriching a massively multilingual database of interlinear glossed text

  • Fei Xia
  • William D. Lewis
  • Michael Wayne Goodman
  • Glenn Slayden
  • Ryan Georgi
  • Joshua Crowgey
  • Emily M. Bender
Original Paper


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 swath of the world’s languages. In many cases this involves bootstrapping the learning process with enriched or partially enriched resources. We propose that Interlinear Glossed Text (IGT), a very common form of annotated data used in the field of linguistics, has great potential for bootstrapping NLP tools for resource-poor languages. 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 describe the expansion of the ODIN resource—a database containing many thousands of instances of IGT for over a thousand languages. We enrich the original IGT data by adding word alignment and syntactic structure. To make the data in ODIN more readily consumable by tool developers and NLP researchers, we adopt and extend a new XML format for IGT, called Xigt. We also develop two packages for manipulating IGT data: one, INTENT, enriches raw IGT automatically, and the other, XigtEdit, is a graphical IGT editor.


Resource-poor languages Interlinear glossed text ODIN 



This material is partly supported by the National Science Foundation under Grant No. BCS-1160274 and BCS-0748919, and Singapore Ministry of Education under Tier 2 Grant No. MOE2013-T2-1-016. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. We would like to thank Sebastian Nordhoff for discussion on the Xigt format and issues with the original IGT data, and anonymous reviewers for helpful comments. We would also like to thank the Linguist List ( for hosting the ODIN database.


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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Fei Xia
    • 1
  • William D. Lewis
    • 2
  • Michael Wayne Goodman
    • 1
  • Glenn Slayden
    • 1
  • Ryan Georgi
    • 1
  • Joshua Crowgey
    • 1
  • Emily M. Bender
    • 1
  1. 1.University of WashingtonSeattleUSA
  2. 2.Microsoft ResearchRedmondUSA

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