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Designing a Uniform Meaning Representation for Natural Language Processing

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Abstract

In this paper we present Uniform Meaning Representation (UMR), a meaning representation designed to annotate the semantic content of a text. UMR is primarily based on Abstract Meaning Representation (AMR), an annotation framework initially designed for English, but also draws from other meaning representations. UMR extends AMR to other languages, particularly morphologically complex, low-resource languages. UMR also adds features to AMR that are critical to semantic interpretation and enhances AMR by proposing a companion document-level representation that captures linguistic phenomena such as coreference as well as temporal and modal dependencies that potentially go beyond sentence boundaries.

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Notes

  1. As can be seen from this example, the document-level representation is a list of triples in the form of <dependent relation parent>, and deviates from the Penn notation used for the sentence-level representation.

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Authors and Affiliations

  1. University of New Mexico, Albuquerque, NM, USA

    Jens E. L. Van Gysel, Meagan Vigus, William Croft & Rosa Vallejos

  2. Brandeis University, Waltham, MA, USA

    Jayeol Chun, Kenneth Lai, Jiarui Yao, James Pustejovsky & Nianwen Xue

  3. University of Colorado at Boulder, Boulder, CO, USA

    Sarah Moeller, Andrew Cowell, James H. Martin & Martha Palmer

  4. University of Massachusetts at Amherst, Amherst, MA, USA

    Tim O’Gorman

  5. Hong Kong Polytechnic University, Hong Kong, China

    Chu-Ren Huang

  6. Charles University, Prague, Czech Republic

    Jan Hajič

  7. University of Oslo, Oslo, Norway

    Stephan Oepen

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  1. Jens E. L. Van Gysel
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Correspondence to Nianwen Xue.

Additional information

This work is supported in part by a grant from the IIS Division of National Science Foundation (Awards Nos. 1763926, 1764048, 1764091) entitled “Building a Uniform Meaning Representation for Natural Language Processing” awarded to Nianwen Xue, James Pustejovsky, Martha Palmer and William Croft. All views expressed in this paper are those of the authors and do not necessarily represent the view of the National Science Foundation. This work is supported in part by a grant from the Ministry of Education, Youth and Sports of the Czech Republic (Project No. LM2018101) and in part by the Czech Science Foundation (Award No. GX20-16819X).

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Van Gysel, J.E.L., Vigus, M., Chun, J. et al. Designing a Uniform Meaning Representation for Natural Language Processing. Künstl Intell 35, 343–360 (2021). https://doi.org/10.1007/s13218-021-00722-w

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