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.
Highlights
Shallow semantic parsing has focused on tagging predicates expressed by individual lexical units
Based on the data and annotation scheme from Dunietz et al (2015), we developed the Bank of Effects and Causes Stated Explicitly (BECAUSE), which was used for all experiments below
Our most important conclusion from these results is that a classifier can learn to recognize many of the subtleties that distinguish causal constructions from their similar non-causal counterparts
Summary
Shallow semantic parsing has focused on tagging predicates expressed by individual lexical units.
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