Resources for building applications with Dependency Minimal Recursion Semantics
By Ann A. Copestake, Guy Edward Toh Emerson, Michael Wayne Goodman, Matic Horvat, Alexander Kuhnle, Ewa Muszynska. 2016
31 citations recorded by the source corpus.
We describe resources aimed at increasing the usability of the semantic representations utilized within the DELPH-IN (Deep Linguistic Processing with HPSG) consortium. We concentrate in particular on the Dependency Minimal Recursion Semantics (DMRS) formalism, a graph-based representation designed for compositional semantic representation with deep grammars. Our main focus is on English, and specifically English Resource Semantics (ERS) as used in the English Resource Grammar. We first give an introduction to ERS and DMRS and a brief overview of some existing resources and then describe in detail a new repository which has been developed to simplify the use of ERS/DMRS. We explain a number of operations on DMRS graphs which our repository supports, with sketches of the algorithms, and illustrate how these operations can be exploited in application building. We believe that this work will aid researchers to exploit the rich and effective but complex DELPH-IN resources.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jacob Andreas
- Mohit Bansal
- Percy Liang
- Daniel Klein
- Trevor Darrell
- Christopher D. Manning
- Sergey Levine
- Luke S. Zettlemoyer
In Natural Language Processing
- Jacob Andreas
- Daniel Klein
- Alexander Kuhnle
- Sergey Levine
- Ann A. Copestake
- Mohit Bansal
- Christopher D. Manning
- Percy Liang
Citations in the corpus
- Shaping Visual Representations with Language for Few-Shot Classification (2019, neutral): “3 Each task contains a single support set of K = 4 images representing a visual concept with an associated (artificial) English language description, generated with a minimal recursion semantics representation of the concept [7].”
- Learning with Latent Language (2017, methodology): “Captions in this dataset were generated from DMRS representations using an HPS grammar (Copestake et al., 2016).”
- ShapeWorld - A new test methodology for multimodal language understanding (2017, methodology): “Figure 3 shows an example of a more complex compositional caption, which contains the DMRS graph above as sub-graph.”