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        <title>Automatically Tagging Constructions of Causation and Their Slot-Fillers</title>
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        <namePart type="given">Jesse</namePart>
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    <name type="personal">
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    <abstract>This paper explores extending shallow semantic parsing beyond lexical-unit triggers, using causal relations as a test case. Semantic parsing becomes difficult in the face of the wide variety of linguistic realizations that causation can take on. We therefore base our approach on the concept of constructions from the linguistic paradigm known as Construction Grammar (CxG). In CxG, a construction is a form/function pairing that can rely on arbitrary linguistic and semantic features. Rather than codifying all aspects of each construction’s form, as some attempts to employ CxG in NLP have done, we propose methods that offload that problem to machine learning. We describe two supervised approaches for tagging causal constructions and their arguments. Both approaches combine automatically induced pattern-matching rules with statistical classifiers that learn the subtler parameters of the constructions. Our results show that these approaches are promising: they significantly outperform naïve baselines for both construction recognition and cause and effect head matches.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00050</identifier>
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        <date>2017</date>
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%0 Journal Article
%T Automatically Tagging Constructions of Causation and Their Slot-Fillers
%A Dunietz, Jesse
%A Levin, Lori
%A Carbonell, Jaime
%J Transactions of the Association for Computational Linguistics
%D 2017
%V 5
%I MIT Press
%C Cambridge, MA
%F dunietz-etal-2017-automatically
%X This paper explores extending shallow semantic parsing beyond lexical-unit triggers, using causal relations as a test case. Semantic parsing becomes difficult in the face of the wide variety of linguistic realizations that causation can take on. We therefore base our approach on the concept of constructions from the linguistic paradigm known as Construction Grammar (CxG). In CxG, a construction is a form/function pairing that can rely on arbitrary linguistic and semantic features. Rather than codifying all aspects of each construction’s form, as some attempts to employ CxG in NLP have done, we propose methods that offload that problem to machine learning. We describe two supervised approaches for tagging causal constructions and their arguments. Both approaches combine automatically induced pattern-matching rules with statistical classifiers that learn the subtler parameters of the constructions. Our results show that these approaches are promising: they significantly outperform naïve baselines for both construction recognition and cause and effect head matches.
%R 10.1162/tacl_a_00050
%U https://aclanthology.org/Q17-1009
%U https://doi.org/10.1162/tacl_a_00050
%P 117-133

Markdown (Informal)

[Automatically Tagging Constructions of Causation and Their Slot-Fillers](https://aclanthology.org/Q17-1009) (Dunietz et al., TACL 2017)

• Automatically Tagging Constructions of Causation and Their Slot-Fillers (Dunietz et al., TACL 2017)

ACL

• Jesse Dunietz, Lori Levin, and Jaime Carbonell. 2017. Automatically Tagging Constructions of Causation and Their Slot-Fillers. Transactions of the Association for Computational Linguistics, 5:117–133.