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Batiukova Belotserkovskaya, OlgaAuthor

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October 20, 2020
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Annotation of Compositional Operations with GLML

Publicated to:COMPUTING MEANING, VOL 4. 47 217-234 - 2014-01-01 47(), DOI: 10.1007/978-94-007-7284-7_12

Authors: Pustejovsky, James; Rumshisky, Anna; Batiukova, Olga; Moszkowicz, Jessica L

Affiliations

Autonomous Univ Madrid, Dept Spanish Philol, E-28049 Madrid, Spain - Author
Brandeis Univ, Dept Comp Sci, Waltham, MA 02454 USA - Author
MIT, Artificial Intelligence Lab, Cambridge, MA 02139 USA - Author
MIT, Cambridge, MA 02139 USA - Author
Univ Massachusetts, Dept Comp Sci, Lowell, MA 01854 USA - Author
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Abstract

In this paper, we introduce a methodology for annotating compositional operations in natural language text and describe the Generative Lexicon Mark-up Language (GLML), a mark-up language inspired by the Generative Lexicon model, for identifying such relations. While most annotation systems capture surface relationships, GLML captures the compositional history of the argument selection relative to the predicate. We provide a brief overview of GL before moving on to our proposed methodology for annotating with GLML. There are three main tasks described in the paper. The first one is based on atomic semantic types and the other two exploit more fine-grained meaning parameters encoded in the Qualia Structure roles: (i) Argument Selection and Coercion Annotation for the SemEval-2010 competition; (ii) Qualia Selection in modification constructions; (iii) Type selection in modification constructions and verb-noun combinations involving dot objects. We explain what each task comprises and include the XML format for annotated sample sentences. We show that by identifying and subsequently annotating the typing and subtyping shifts in these constructions, we gain an insight into the workings of the general mechanisms of composition.

Keywords

Quality education

Quality index

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-08-02:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 1 (PlumX).
Continuing with the social impact of the work, it is important to emphasize that, due to its content, it can be assigned to the area of interest of ODS 4 - Quality Education, with a probability of 78% according to the mBERT algorithm developed by Aurora University.

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: United States of America.