Abstract
Frame semantics is a theory of meaning in natural language, which defines the structure of the lexical semantic resources known as framenets. Both framenets and frame semantics have proved useful for a number of natural language processing (NLP) tasks. However, in this connection framenets have often been criticized for their limited coverage. A proposed reasonable-effort solution to this problem is to develop domain-specific (sublanguage) framenets to complement the corresponding general-language framenets for particular NLP tasks, and in the literature we find such initiatives covering domains such as medicine, soccer, and tourism. In this paper, we report on building a framenet to cover the terms and concepts encountered in descriptive linguistic grammars (written in English) i.e. a framenet for the linguistic domain (LingFN) to complement the general-language BFN.
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Notes
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Labels of FEs and frames are conventionally set in small caps.
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Most of the framenets – including BFN – have been developed in the context of linguistic lexicology, even if several of them have been used in NLP applications (again including BFN). The Swedish FrameNet (SweFN) forms a notable exception in this regard, having been built from the outset as a lexical resource for NLP use and only secondarily serving purposes of linguistic research [2, 6].
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Acknowledgments
The work presented here was funded partially by the Swedish Research Council as part of the project South Asia as a linguistic area? Exploring big-data methods in areal and genetic linguistics (2015–2019, contract no. 421-2014-969), and partially by the Dictionary/Grammar Reading Machine: Computational Tools for Accessing the World’s Linguistic Heritage (DReaM) Project awarded 2018-2010 by the Joint Programming Initiative in Cultural Heritage and Global Change, Digital Heritage and Riksantikvarieämbetet, Sweden.
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Virk, S.M., Klang, P., Borin, L., Saxena, A. (2023). LingFN: A Framenet for the Linguistic Domain. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13451. Springer, Cham. https://doi.org/10.1007/978-3-031-24337-0_27
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