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A computational construction grammar approach to semantic frame extraction

Katrien Beuls ORCID logo, Paul Van Eecke ORCID logo and Vanja Sophie Cangalovic
From the journal Linguistics Vanguard

Abstract

This paper introduces a novel methodology for extracting semantic frames from text corpora. Building on recent advances in computational construction grammar, the method captures expert knowledge of how semantic frames can be expressed in the form of conventionalised form-meaning pairings, called constructions. By combining these constructions in a semantic parsing process, the frame-semantic structure of a sentence is retrieved through the intermediary of its morpho-syntactic structure. The main advantage of this approach is that state-of-the-art results are achieved, without the need for annotated training data. We demonstrate the method in a case study where causation frames are extracted from English newspaper articles, and compare it to a commonly used approach based on Conditional Random Fields (CRFs). The computational construction grammar approach yields a word-level F1 score of 78.5%, outperforming the CRF approach by 4.5 percentage points.


Corresponding author: Katrien Beuls, Artificial Intelligence Laboratory, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium, E-mail:

Funding source: Vlaamse Overheid

Funding source: H2020 Future and Emerging Technologies

Award Identifier / Grant number: 732942

Funding source: Fonds Wetenschappelijk Onderzoek

Award Identifier / Grant number: 75929

Acknowledgment

We would like to thank Luc Steels for his valuable feedback on this work and Remi van Trijp for his work as area editor for Linguistics Vanguard.

  1. Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732942 (funder id: http://dx.doi.org/10.13039/100010664), from the Flemish Government under the ‘Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen’ programme, and from a postdoctoral fellowship of the Research Foundation Flanders (FWO) awarded to PVE (grant No 75929, funder id: http://dx.doi.org/10.13039/501100003130).

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Received: 2020-02-23
Accepted: 2020-12-21
Published Online: 2021-02-18

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